The "Forecasting Sales Data Using Statistical Modeling and Machine Learning" project revolves around harnessing US sales data from 1992 to 2020 to predict future sales trends and analyze the impact of significant crises. Sourced meticulously from the US Census Bureau, the data underwent rigorous monitoring to ensure quality and accuracy in the estimates representing various sectors of the US economy.
Through the application of Time-series Analysis techniques, the project aimed to unveil valuable insights buried within the sales data and produce reliable sales forecasts for the coming years. A comprehensive set of tasks was undertaken, encompassing data collection, rigorous data cleaning, and compelling data visualizations to understand the trends and patterns better.
The project's core entailed statistical methods and advanced Machine Learning models to perform robust sales predictions. Leveraging the power of predictive analytics, the team sought to equip businesses with the knowledge to make informed decisions and plan effectively for the future.
Moreover, the project's unique feature was the in-depth exploration of the impact of significant crises, such as the 2008 Economic crisis and the recent COVID-19 pandemic, on the retail sector. By examining sales patterns during these periods, the team aimed to gain critical insights into the sector's resilience and adaptability during times of upheaval.
The project's findings were presented in a detailed and comprehensive report, offering a complete overview of the methodologies employed, insights gained, and forecasts generated. As a result, this project contributes valuable knowledge to sales forecasting and crisis impact analysis, providing a solid foundation for making strategic decisions in the retail industry.
More on https://highlyscalable.wordpress.com/
Data Mining Problems in Retail is an analytical report that studies how retailers can make sense of their
data by adopting advanced data analysis and optimization techniques that enable automated decision
making in the area of marketing and pricing. The report analyzes dozens of practical case studies and
research reports and presents a systematic view on the problem.
We hope that this article will be useful for data scientists, marketing specialists, and business analysts
who are looking beyond the basic statistical and data mining techniques to build comprehensive
data-driven business optimization processes and solutions.
Visuals present better and quicker insights when forecasting sales. At a glance business strategies can be planned - time periods, geographic locations, pick variables that can highlight what works or doesn't, where it scores or doesn't, join two or more variables that work in specific geographical locations or don't, etc. All this put together makes data virtualization a very nifty tool to project what can make or break your predictions for sales!
A Sales Forecasting Model Based on Internal Organizational Variables.pdfAnna Landers
This document describes a sales forecasting model developed for a small export business unit based on internal organizational variables. The model uses an econometric technique and compares its outputs to simpler univariate forecasting techniques used by the organization. The econometric model produces a better fit to observable data and integrates quantitative variables accounting for relevant management decisions. It can reasonably explain sales that would not be explained by external economic variables alone. The model focuses exclusively on observable explanatory variables internal to the organization, which could facilitate its implementation in other similar organizations.
MA- UNIT -1.pptx for ipu bba sem 5, complete pdfzm2pfgpcdt
Marketing analytics is the practice of using data to evaluate the effectiveness and success of marketing activities. It allows marketers to gather deeper consumer insights, optimize marketing objectives, and get a better return on investment. Popular analytics models include media mix models, multi-touch attribution, and unified marketing measurement. Organizations use marketing analytics data to make decisions regarding ad spend, product updates, branding, and more. Common predictive analytics techniques used in marketing include decision trees, regression, and neural networks.
This document provides an introduction to analytics. It discusses how analytics uses data, information technology, statistical analysis and models to help managers make better decisions. Some potential applications of analytics discussed include pricing, customer segmentation, merchandising and location selection. The document also discusses descriptive, predictive and prescriptive analytics and some common analytics tools and challenges. It provides an overview of how analytics can be used to solve business problems.
Data Drive Better Sales Conversions - Dawn of the Data Age Lecture SeriesLuciano Pesci, PhD
Sales is the lifeblood of any organization, and in today’s increasingly data-driven world, sales teams are often the last to adapt and change to a data-driven strategy. The skepticism of sales teams is likely due to lack of data scientists failing to deliver insights that are digestible to sales teams and that sellers can take action from. Fortunately, becoming a data-driven sales team isn't impossible, it just requires the right mix of human data-detective work and a touch of automation to create a scalable system to deliver leads that convert at higher rates and at higher total value to an organization.
This Lecture Will:
-TEACH YOU THE ROADBLOCKS SALES TEAMS HIT WITH DATA.
-SHOW YOU DATA TYPES & USES FOR BETTER SALES CONVERSIONS.
-EXPLAIN HOW TO BECOME A DATA-DRIVEN SALES LEADER.
You can watch this lecture here: https://youtu.be/noIjGerm3eE
Demand forecasting involves using statistical data and analysis to predict future demand for a product. There are different types of forecasts including short term (less than 1 year), long term, and passive vs active. Short term forecasts help with sales, pricing, and target policies while long term helps with planning. Demand can be forecast at the macro, industry, or firm level. Statistical methods include time series analysis, regression analysis, and smoothing techniques like moving averages and exponential smoothing. Accurate demand forecasting is important for production, inventory, investment, and economic planning.
Data-Driven Decisions: Unraveling Business Insights Through Research Data Ana...UnitedInnovator
Understanding the value of research data analysis for organisations is essential in the age of data-driven decision-making. This extensive guide digs into the area of decoding research data analysis and provides readers with key skills to elucidate insightful business information. Learn how to gather, arrange, and analyse data from various sources to spot trends, correlations, and patterns. Learn how to transform raw data into useful insight by utilising statistical analysis and data visualisation tools. This tool equips professionals with the tools they need to harness the potential of research data analysis and acquire a competitive edge in the quickly changing business environment of today. Its focus is on promoting informed decision-making.
More on https://highlyscalable.wordpress.com/
Data Mining Problems in Retail is an analytical report that studies how retailers can make sense of their
data by adopting advanced data analysis and optimization techniques that enable automated decision
making in the area of marketing and pricing. The report analyzes dozens of practical case studies and
research reports and presents a systematic view on the problem.
We hope that this article will be useful for data scientists, marketing specialists, and business analysts
who are looking beyond the basic statistical and data mining techniques to build comprehensive
data-driven business optimization processes and solutions.
Visuals present better and quicker insights when forecasting sales. At a glance business strategies can be planned - time periods, geographic locations, pick variables that can highlight what works or doesn't, where it scores or doesn't, join two or more variables that work in specific geographical locations or don't, etc. All this put together makes data virtualization a very nifty tool to project what can make or break your predictions for sales!
A Sales Forecasting Model Based on Internal Organizational Variables.pdfAnna Landers
This document describes a sales forecasting model developed for a small export business unit based on internal organizational variables. The model uses an econometric technique and compares its outputs to simpler univariate forecasting techniques used by the organization. The econometric model produces a better fit to observable data and integrates quantitative variables accounting for relevant management decisions. It can reasonably explain sales that would not be explained by external economic variables alone. The model focuses exclusively on observable explanatory variables internal to the organization, which could facilitate its implementation in other similar organizations.
MA- UNIT -1.pptx for ipu bba sem 5, complete pdfzm2pfgpcdt
Marketing analytics is the practice of using data to evaluate the effectiveness and success of marketing activities. It allows marketers to gather deeper consumer insights, optimize marketing objectives, and get a better return on investment. Popular analytics models include media mix models, multi-touch attribution, and unified marketing measurement. Organizations use marketing analytics data to make decisions regarding ad spend, product updates, branding, and more. Common predictive analytics techniques used in marketing include decision trees, regression, and neural networks.
This document provides an introduction to analytics. It discusses how analytics uses data, information technology, statistical analysis and models to help managers make better decisions. Some potential applications of analytics discussed include pricing, customer segmentation, merchandising and location selection. The document also discusses descriptive, predictive and prescriptive analytics and some common analytics tools and challenges. It provides an overview of how analytics can be used to solve business problems.
Data Drive Better Sales Conversions - Dawn of the Data Age Lecture SeriesLuciano Pesci, PhD
Sales is the lifeblood of any organization, and in today’s increasingly data-driven world, sales teams are often the last to adapt and change to a data-driven strategy. The skepticism of sales teams is likely due to lack of data scientists failing to deliver insights that are digestible to sales teams and that sellers can take action from. Fortunately, becoming a data-driven sales team isn't impossible, it just requires the right mix of human data-detective work and a touch of automation to create a scalable system to deliver leads that convert at higher rates and at higher total value to an organization.
This Lecture Will:
-TEACH YOU THE ROADBLOCKS SALES TEAMS HIT WITH DATA.
-SHOW YOU DATA TYPES & USES FOR BETTER SALES CONVERSIONS.
-EXPLAIN HOW TO BECOME A DATA-DRIVEN SALES LEADER.
You can watch this lecture here: https://youtu.be/noIjGerm3eE
Demand forecasting involves using statistical data and analysis to predict future demand for a product. There are different types of forecasts including short term (less than 1 year), long term, and passive vs active. Short term forecasts help with sales, pricing, and target policies while long term helps with planning. Demand can be forecast at the macro, industry, or firm level. Statistical methods include time series analysis, regression analysis, and smoothing techniques like moving averages and exponential smoothing. Accurate demand forecasting is important for production, inventory, investment, and economic planning.
Data-Driven Decisions: Unraveling Business Insights Through Research Data Ana...UnitedInnovator
Understanding the value of research data analysis for organisations is essential in the age of data-driven decision-making. This extensive guide digs into the area of decoding research data analysis and provides readers with key skills to elucidate insightful business information. Learn how to gather, arrange, and analyse data from various sources to spot trends, correlations, and patterns. Learn how to transform raw data into useful insight by utilising statistical analysis and data visualisation tools. This tool equips professionals with the tools they need to harness the potential of research data analysis and acquire a competitive edge in the quickly changing business environment of today. Its focus is on promoting informed decision-making.
This document discusses various forecasting techniques used to predict future demand and sales. It describes qualitative methods like executive opinion and the Delphi method that rely on expert judgment. It also covers quantitative time series methods like moving averages, exponential smoothing, and trend projection that analyze historical data patterns. The document explains how to incorporate factors like trends, seasonality, and product life cycles into forecasts and evaluates accuracy using metrics like mean absolute deviation.
The document provides an overview of a 3-day data analytics training program held in Jakarta, Indonesia from April 24-26, 2019. It discusses topics that will be covered including big data overview, data for business analysis, data analytics concepts, and data analytics tools. The training is led by Dr. Ir. John Sihotang and is aimed at management trainees of the company Sucofindo.
Demand forecasting aims to understand and predict consumer demand for goods. Accurately forecasting demand is important for efficient supply chain management. Errors in demand forecasting can lead to lost sales from underestimating demand or excess inventory from overestimating demand. Various statistical and machine learning techniques can be used to model consumer demand patterns and generate forecasts, including time series analysis, neural networks, and data mining algorithms. Proper data preparation is critical for generating accurate demand forecast models.
Business Analytics models, measuring scales etc.pptxfaizhasan406
This document discusses business analytics and its applications. It defines business analytics as the scientific process of transforming data into insights to make better fact-based decisions. The document outlines different types of analytics including descriptive analytics which describes past performance, predictive analytics which predicts the future based on patterns in historical data, and prescriptive analytics which recommends optimal decisions. Examples of applying analytics to pricing, customer segmentation, merchandising, and other business functions are provided. The benefits and challenges of analytics are also summarized.
This document provides an overview of analytics. It defines analytics as using data, technology, analysis and models to help managers make better decisions. It discusses different types of analytics including descriptive, predictive and prescriptive. Descriptive analytics examines past performance, predictive analytics predicts the future by detecting patterns in data, and prescriptive analytics identifies the best alternatives. The document also briefly covers tools, data, models, and using analytics to solve business problems.
This document provides an overview of analytics. It defines analytics as using data, technology, analysis and models to help managers make better decisions. It discusses different types of analytics including descriptive, predictive and prescriptive. Descriptive analytics examines past performance, predictive analytics predicts the future by detecting patterns in data, and prescriptive analytics identifies the best alternatives. The document also briefly covers tools, data, models, and using analytics to solve business problems.
Chapter 1 Introduction to Business Analytics.pdfShamshadAli58
This document provides an overview of analytics. It defines analytics as using data, technology, analysis and models to help managers make better decisions. It discusses different types of analytics including descriptive, predictive and prescriptive. Descriptive analytics examines past performance, predictive analytics predicts the future by detecting patterns in data, and prescriptive analytics identifies the best alternatives. The document also covers topics such as tools, data, models, and using analytics to solve business problems.
This document discusses analyzing time series data regression through a practical example. It explains that regression analysis helps identify relationships between variables and make predictions by examining historical trends and patterns in time series data. As an example, it describes how a retail business could analyze monthly sales data over five years to build a regression model to accurately forecast future sales and make better inventory, marketing, and business planning decisions. The document outlines the key steps in time series regression analysis, including preprocessing data, selecting the appropriate regression model, evaluating model performance, and interpreting regression results.
This document discusses analyzing time series data regression through a practical example. It explains that regression analysis helps identify relationships between variables and make predictions by examining historical trends and patterns in time series data. As an example, it describes how a retail business could analyze monthly sales data over five years to build a regression model to accurately forecast future sales and make better inventory, marketing, and business planning decisions. The document outlines the key steps in time series regression analysis, including preprocessing data, selecting the appropriate regression model, evaluating model performance, and interpreting regression results.
BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Business analytics examples
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
Introduction toDemand Forecasting part oneErichViray
This document discusses demand forecasting, including its meaning, purpose, scope, methods, and significance. Demand forecasting predicts future sales trends based on current demand determinants. It is used for both short-term and long-term planning purposes. Determining the appropriate scope involves factors like the forecast period, level, purpose, product type, and relevant demand factors. The forecasting process involves identifying objectives, evaluating the product type, selecting a forecasting method, and interpreting results. Accurate demand forecasts are important for fulfilling objectives, budgeting, production planning, expansion decisions, and performance evaluation.
The document discusses how various types of analytics, including descriptive, predictive, and prescriptive analytics can be applied across different business functions and industries. Descriptive analytics are used to understand past performance, predictive analytics analyze past data to predict future outcomes, and prescriptive analytics use optimization techniques to recommend decisions. Examples are provided of how different industries like retail use different analytic techniques to improve operations and decision making.
Statistics, Data Analysis, and Decision ModelingFOURTH EDITION.docxdessiechisomjj4
Statistics, Data Analysis, and Decision Modeling
FOURTH EDITION
James R. Evans
9780558689766
Chapter 7 Forecasting
Introduction
QUALITATIVE AND JUDGMENTAL METHODS
Historical Analogy
The Delphi Method
Indicators and Indexes for Forecasting
STATISTICAL FORECASTING MODELS
FORECASTING MODELS FOR STATIONARY TIME SERIES
Moving Average Models
Error Metrics and Forecast Accuracy
Exponential Smoothing Models
FORECASTING MODELS FOR TIME SERIES WITH TREND AND SEASONALITY
Models for Linear Trends
Models for Seasonality
Models for Trend and Seasonality
CHOOSING AND OPTIMIZING FORECASTING MODELS USING CB PREDICTOR
REGRESSION MODELS FOR FORECASTING
Autoregressive Forecasting Models
Incorporating Seasonality in Regression Models
Regression Forecasting with Causal Variables
THE PRACTICE OF FORECASTING
BASIC CONCEPTS REVIEW QUESTIONS
SKILL-BUILDING EXERCISES
SKILL-BUILDING EXERCISES
PROBLEMS AND APPLICATIONS
CASE: ENERGY FORECASTING
APPENDIX: ADVANCED FORECASTING MODELS—THEORY AND COMPUTATION
Double Moving Average
Double Exponential Smoothing
Additive Seasonality
Multiplicative Seasonality
Holt–Winters Additive Model
Holt– –Winters Multiplicative Model
INTRODUCTION
One of the major problems that managers face is forecasting future events in order to make good decisions. For example, forecasts of interest rates, energy prices, and other economic indicators are needed for financial planning; sales forecasts are needed to plan production and workforce capacity; and forecasts of trends in demographics, consumer behavior, and technological innovation are needed for long-term strategic planning. The government also invests significant resources on predicting short-run U.S. business performance using the Index of Leading Indicators. This index focuses on the performance of individual businesses, which often is highly correlated with the performance of the overall economy, and is used to forecast economic trends for the nation as a whole. In this chapter, we introduce some common methods and approaches to forecasting, including both qualitative and quantitative techniques.
Managers may choose from a wide range of forecasting techniques. Selecting the appropriate method depends on the characteristics of the forecasting problem, such as the time horizon of the variable being forecast, as well as available information on which the forecast will be based. Three major categories of forecasting approaches are qualitative and judgmental techniques, statistical time-series models, and explanatory/causal methods.
Qualitative and judgmental techniques rely on experience and intuition; they are necessary when historical data are not available or when the decision maker needs to forecast far into the future. For example, a forecast of when the next generation of a microprocessor will be available and what capabilities it might have will depend greatly on the opinions and expertise of individuals who understand the technology.
Statistical time-series models find g.
Demand forecasting is used to estimate future demand for a product or service based on an analysis of past demand and current market conditions. There are several statistical methods used for demand forecasting, including trend projection, which fits a trend line to past sales data to project future trends. The barometric technique uses current economic and statistical indicators to predict future changes in demand. Econometric models use a system of independent regression equations to model relationships between economic variables and forecast demand.
This document discusses forecasting techniques. It begins by defining forecasting as predicting future events using historical data and mathematical models. It then discusses different forecasting time horizons including short, medium, and long range. Short range forecasts are less than 1 year, medium 1-3 years, and long more than 3 years. The document also covers qualitative and quantitative forecasting approaches, types of forecasts including economic, technological, and demand, and examples of forecasting techniques like moving averages and exponential smoothing.
Demand Forecasting in the restaurant managementErichViray
Demand forecasting predicts future sales trends based on current demand determinants. It is important for business planning purposes. There are several key steps to demand forecasting including determining the objective, nature of goods, appropriate forecasting method, and interpreting results. The period, level, purpose, product type, and other factors must be defined. Demand forecasts help businesses fulfill objectives, prepare budgets, stabilize production and employment, expand, make decisions, and evaluate performance. They are significant for both short and long-term business planning.
This document contains a detailed analysis of FMCG sales data given as a case study project. It discusses data cleaning steps like multiple imputation to estimate missing values and converting quarterly GDP growth rates to monthly values. A dynamic regression model is used to analyze how sales are impacted by factors like crude oil prices, sugar prices, GDP, CPI, PPI, and IPI. The analysis finds that increases in sugar prices, GDP growth, PPI and IPI negatively impact sales changes per store over time.
Lesson 1 - Overview of Machine Learning and Data Analysis.pptxcloudserviceuit
This document provides an overview of machine learning and data analysis. It defines machine learning as a field of artificial intelligence that enables computers to learn from data without being explicitly programmed. The main types of machine learning are supervised, unsupervised, and reinforcement learning. Data analysis is the process of extracting meaningful insights from data through techniques like cleaning, exploring for patterns/trends, statistical analysis, and visualization. Machine learning automates many data analysis tasks and can be applied through techniques like classification, clustering, and regression. The relationship between machine learning and data analysis fuels discovery, with data analysis providing foundation and machine learning generating insights.
This report contains:-
1. what is data analytics, its usages, its types.
2. Tools used for data analytics
3. description of Classification
4. description of the association
5. description of clustering
6. decision tree, SVM modelling etc with example
This chapter introduces econometrics and its application to finance. Econometrics involves using statistical and mathematical techniques to solve financial problems, such as testing models of asset returns, measuring volatility, and forecasting correlations. Financial data has special characteristics like high frequency, potential measurement errors, and distinctions between continuous and discrete data types. The chapter outlines approaches for modeling time series, cross-sectional, and panel financial data, as well as for working with returns rather than raw prices. It also reviews considerations for developing, estimating, and evaluating econometric models.
This document discusses various forecasting techniques used to predict future demand and sales. It describes qualitative methods like executive opinion and the Delphi method that rely on expert judgment. It also covers quantitative time series methods like moving averages, exponential smoothing, and trend projection that analyze historical data patterns. The document explains how to incorporate factors like trends, seasonality, and product life cycles into forecasts and evaluates accuracy using metrics like mean absolute deviation.
The document provides an overview of a 3-day data analytics training program held in Jakarta, Indonesia from April 24-26, 2019. It discusses topics that will be covered including big data overview, data for business analysis, data analytics concepts, and data analytics tools. The training is led by Dr. Ir. John Sihotang and is aimed at management trainees of the company Sucofindo.
Demand forecasting aims to understand and predict consumer demand for goods. Accurately forecasting demand is important for efficient supply chain management. Errors in demand forecasting can lead to lost sales from underestimating demand or excess inventory from overestimating demand. Various statistical and machine learning techniques can be used to model consumer demand patterns and generate forecasts, including time series analysis, neural networks, and data mining algorithms. Proper data preparation is critical for generating accurate demand forecast models.
Business Analytics models, measuring scales etc.pptxfaizhasan406
This document discusses business analytics and its applications. It defines business analytics as the scientific process of transforming data into insights to make better fact-based decisions. The document outlines different types of analytics including descriptive analytics which describes past performance, predictive analytics which predicts the future based on patterns in historical data, and prescriptive analytics which recommends optimal decisions. Examples of applying analytics to pricing, customer segmentation, merchandising, and other business functions are provided. The benefits and challenges of analytics are also summarized.
This document provides an overview of analytics. It defines analytics as using data, technology, analysis and models to help managers make better decisions. It discusses different types of analytics including descriptive, predictive and prescriptive. Descriptive analytics examines past performance, predictive analytics predicts the future by detecting patterns in data, and prescriptive analytics identifies the best alternatives. The document also briefly covers tools, data, models, and using analytics to solve business problems.
This document provides an overview of analytics. It defines analytics as using data, technology, analysis and models to help managers make better decisions. It discusses different types of analytics including descriptive, predictive and prescriptive. Descriptive analytics examines past performance, predictive analytics predicts the future by detecting patterns in data, and prescriptive analytics identifies the best alternatives. The document also briefly covers tools, data, models, and using analytics to solve business problems.
Chapter 1 Introduction to Business Analytics.pdfShamshadAli58
This document provides an overview of analytics. It defines analytics as using data, technology, analysis and models to help managers make better decisions. It discusses different types of analytics including descriptive, predictive and prescriptive. Descriptive analytics examines past performance, predictive analytics predicts the future by detecting patterns in data, and prescriptive analytics identifies the best alternatives. The document also covers topics such as tools, data, models, and using analytics to solve business problems.
This document discusses analyzing time series data regression through a practical example. It explains that regression analysis helps identify relationships between variables and make predictions by examining historical trends and patterns in time series data. As an example, it describes how a retail business could analyze monthly sales data over five years to build a regression model to accurately forecast future sales and make better inventory, marketing, and business planning decisions. The document outlines the key steps in time series regression analysis, including preprocessing data, selecting the appropriate regression model, evaluating model performance, and interpreting regression results.
This document discusses analyzing time series data regression through a practical example. It explains that regression analysis helps identify relationships between variables and make predictions by examining historical trends and patterns in time series data. As an example, it describes how a retail business could analyze monthly sales data over five years to build a regression model to accurately forecast future sales and make better inventory, marketing, and business planning decisions. The document outlines the key steps in time series regression analysis, including preprocessing data, selecting the appropriate regression model, evaluating model performance, and interpreting regression results.
BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Business analytics examples
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
Introduction toDemand Forecasting part oneErichViray
This document discusses demand forecasting, including its meaning, purpose, scope, methods, and significance. Demand forecasting predicts future sales trends based on current demand determinants. It is used for both short-term and long-term planning purposes. Determining the appropriate scope involves factors like the forecast period, level, purpose, product type, and relevant demand factors. The forecasting process involves identifying objectives, evaluating the product type, selecting a forecasting method, and interpreting results. Accurate demand forecasts are important for fulfilling objectives, budgeting, production planning, expansion decisions, and performance evaluation.
The document discusses how various types of analytics, including descriptive, predictive, and prescriptive analytics can be applied across different business functions and industries. Descriptive analytics are used to understand past performance, predictive analytics analyze past data to predict future outcomes, and prescriptive analytics use optimization techniques to recommend decisions. Examples are provided of how different industries like retail use different analytic techniques to improve operations and decision making.
Statistics, Data Analysis, and Decision ModelingFOURTH EDITION.docxdessiechisomjj4
Statistics, Data Analysis, and Decision Modeling
FOURTH EDITION
James R. Evans
9780558689766
Chapter 7 Forecasting
Introduction
QUALITATIVE AND JUDGMENTAL METHODS
Historical Analogy
The Delphi Method
Indicators and Indexes for Forecasting
STATISTICAL FORECASTING MODELS
FORECASTING MODELS FOR STATIONARY TIME SERIES
Moving Average Models
Error Metrics and Forecast Accuracy
Exponential Smoothing Models
FORECASTING MODELS FOR TIME SERIES WITH TREND AND SEASONALITY
Models for Linear Trends
Models for Seasonality
Models for Trend and Seasonality
CHOOSING AND OPTIMIZING FORECASTING MODELS USING CB PREDICTOR
REGRESSION MODELS FOR FORECASTING
Autoregressive Forecasting Models
Incorporating Seasonality in Regression Models
Regression Forecasting with Causal Variables
THE PRACTICE OF FORECASTING
BASIC CONCEPTS REVIEW QUESTIONS
SKILL-BUILDING EXERCISES
SKILL-BUILDING EXERCISES
PROBLEMS AND APPLICATIONS
CASE: ENERGY FORECASTING
APPENDIX: ADVANCED FORECASTING MODELS—THEORY AND COMPUTATION
Double Moving Average
Double Exponential Smoothing
Additive Seasonality
Multiplicative Seasonality
Holt–Winters Additive Model
Holt– –Winters Multiplicative Model
INTRODUCTION
One of the major problems that managers face is forecasting future events in order to make good decisions. For example, forecasts of interest rates, energy prices, and other economic indicators are needed for financial planning; sales forecasts are needed to plan production and workforce capacity; and forecasts of trends in demographics, consumer behavior, and technological innovation are needed for long-term strategic planning. The government also invests significant resources on predicting short-run U.S. business performance using the Index of Leading Indicators. This index focuses on the performance of individual businesses, which often is highly correlated with the performance of the overall economy, and is used to forecast economic trends for the nation as a whole. In this chapter, we introduce some common methods and approaches to forecasting, including both qualitative and quantitative techniques.
Managers may choose from a wide range of forecasting techniques. Selecting the appropriate method depends on the characteristics of the forecasting problem, such as the time horizon of the variable being forecast, as well as available information on which the forecast will be based. Three major categories of forecasting approaches are qualitative and judgmental techniques, statistical time-series models, and explanatory/causal methods.
Qualitative and judgmental techniques rely on experience and intuition; they are necessary when historical data are not available or when the decision maker needs to forecast far into the future. For example, a forecast of when the next generation of a microprocessor will be available and what capabilities it might have will depend greatly on the opinions and expertise of individuals who understand the technology.
Statistical time-series models find g.
Demand forecasting is used to estimate future demand for a product or service based on an analysis of past demand and current market conditions. There are several statistical methods used for demand forecasting, including trend projection, which fits a trend line to past sales data to project future trends. The barometric technique uses current economic and statistical indicators to predict future changes in demand. Econometric models use a system of independent regression equations to model relationships between economic variables and forecast demand.
This document discusses forecasting techniques. It begins by defining forecasting as predicting future events using historical data and mathematical models. It then discusses different forecasting time horizons including short, medium, and long range. Short range forecasts are less than 1 year, medium 1-3 years, and long more than 3 years. The document also covers qualitative and quantitative forecasting approaches, types of forecasts including economic, technological, and demand, and examples of forecasting techniques like moving averages and exponential smoothing.
Demand Forecasting in the restaurant managementErichViray
Demand forecasting predicts future sales trends based on current demand determinants. It is important for business planning purposes. There are several key steps to demand forecasting including determining the objective, nature of goods, appropriate forecasting method, and interpreting results. The period, level, purpose, product type, and other factors must be defined. Demand forecasts help businesses fulfill objectives, prepare budgets, stabilize production and employment, expand, make decisions, and evaluate performance. They are significant for both short and long-term business planning.
This document contains a detailed analysis of FMCG sales data given as a case study project. It discusses data cleaning steps like multiple imputation to estimate missing values and converting quarterly GDP growth rates to monthly values. A dynamic regression model is used to analyze how sales are impacted by factors like crude oil prices, sugar prices, GDP, CPI, PPI, and IPI. The analysis finds that increases in sugar prices, GDP growth, PPI and IPI negatively impact sales changes per store over time.
Lesson 1 - Overview of Machine Learning and Data Analysis.pptxcloudserviceuit
This document provides an overview of machine learning and data analysis. It defines machine learning as a field of artificial intelligence that enables computers to learn from data without being explicitly programmed. The main types of machine learning are supervised, unsupervised, and reinforcement learning. Data analysis is the process of extracting meaningful insights from data through techniques like cleaning, exploring for patterns/trends, statistical analysis, and visualization. Machine learning automates many data analysis tasks and can be applied through techniques like classification, clustering, and regression. The relationship between machine learning and data analysis fuels discovery, with data analysis providing foundation and machine learning generating insights.
This report contains:-
1. what is data analytics, its usages, its types.
2. Tools used for data analytics
3. description of Classification
4. description of the association
5. description of clustering
6. decision tree, SVM modelling etc with example
This chapter introduces econometrics and its application to finance. Econometrics involves using statistical and mathematical techniques to solve financial problems, such as testing models of asset returns, measuring volatility, and forecasting correlations. Financial data has special characteristics like high frequency, potential measurement errors, and distinctions between continuous and discrete data types. The chapter outlines approaches for modeling time series, cross-sectional, and panel financial data, as well as for working with returns rather than raw prices. It also reviews considerations for developing, estimating, and evaluating econometric models.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
2. Table of Content
Project objective Data Due Diligence
Background and context Analysis of Data
USA Retail sales data Modeling The Time Series
3. Project objective
Forecasting Sales Data Using Statistical
Modeling and Machine Learning
Here we are using US sales data across various categories from 1992 - 2020, to
predict future sales and find out the effect of some crisis along the way
5. USA Retail sales data
1
The data was collected from the US
census Bureau. Census Bureau
monitored response and data quality
and determined estimates for
various sector of USA economy.
2
The data collected is from 1992 -
2020. Here we are using Time-series
Analysis to find insights and forecast
the sales estimates.
3
We also find the impact of various
major crisis along the way such as
2008 Economic crisis, and more
recently COVID-19 and it impact on
retail sector
4
We have done various tasks for this
data such as data collection,
cleaning, visualization, data analysis,
Prediction (both statistical methods
and Machine learning models for
forecasting)
7. Time-Series Analysis
Time-series data can be defined in many ways. A
simple definition can be “data collected on the
same metric or same object at regular or irregular
time intervals.”
Do we need Time series Analysis:
We should think about whether there is an
inherent relationship or structure between data
at various time points (e.g., is there a
time-dependence), and whether we can leverage
that time-ordered information.
8. Understanding the
data
The dataset contains the estimates of Monthly Retail and Food
Services Sales by Kind of Business from the year 1992 - 2020.
These estimates are shown in millions of dollars and are based on
data from the Monthly Retail Trade Survey, Annual Retail Trade
Survey, * Service Annual Survey, and administrative records.
The dataset contains both adjusted and unadjusted for seasonal
variations for various categories. These categories shows various
kind of Business categories operating in USA. These categories
are based on North American Industry Classification System
(NAICS).
9. Data Levels
● The data was organised using North American Industry Classification System
(NAICS) coding system. It contains multi level categorization of Businesses including:
○ Top level Sales Estimates: Total Estimates of Sales of US as a whole. Ex. 44X72: Retail Trade
and Food Services: U.S. Total
○ First Level : Sales Estimates of categories. Ex. 441:Motor vehicle and parts dealers,
442:Furniture and home furnishings stores
○ Second level : Ex. 4411: Automobile dealers, 4413: Automotive parts, acc., and tire stores.
○ Third Level: Ex. 44111: New car dealers, 44112: Used car dealers
● We need to separate these categories and perform Time series Analysis on each
particular levels separately
10. Handling inconsistencies in our data
● Following are the inconsistencies that was in our data:
○ (S) Suppressed - Estimate does not meet publication standards because of high sampling
variability (coefficient of variation is greater than 30%), poor response quality (total quantity
response rate is less than 50%), or other concerns about the estimate quality.
○ (NA) Not available
● Since our data is Estimate of sales we have suppressed values in our data. We can
handle these values by predicting these values from the past values
● Not available values can also be predicted using the same techniques we used for
Suppressed values
● Handling the different data types and converting them into datetime and float64 was
also one of the tasks
11. What questions do we want to Answer?
● I think that it is important to start with a very concrete question that we think can be
answered with data, specifically with time-series data.
● Some of the questions that we might answer with this forecasting:
○ Forecast future sales Estimate of retail sector of USA.
○ Forecast future sales Estimate of different categories in retail Sector.
● Understand the limitations of the data and what potential questions can be answered
by data is important. These questions can reduce, expand, or modify the scope of our
project.
12. What techniques may help us to answer these
Questions ?
Statistical models
● Ignore the time-series aspect completely and model using traditional statistical modeling
toolbox. Examples. Regression-based models.
● Univariate statistical time-series modeling. Examples. Averaging and smoothing models,
ARIMA models.
● Slight modifications to univariate statistical time-series modeling. Examples. External
regressors, multivariate models.
● Additive or component models. Examples. Facebook Prophet package.
● Structural time series modeling.Examples. Bayesian structural time series modeling,
hierarchical time series modeling.
13. What techniques may help us to answer these
Questions ?
Machine learning models
● Ignore the time-series aspect completely and model using traditional machine
learning modeling toolbox. Examples. Support Vector Machines (SVMs), Random
Forest Regression, Gradient-Boosted Decision Trees (GBDTs), Neural Networks
(NNs)
● Hidden markov models (HMMs).
● Other sequence-based models.
● Gaussian processes (GPs).
● Recurrent neural networks (RNNs).
14. What techniques may help us to answer these
Questions ?
Additional data considerations before choosing a model
● Whether or not to incorporate external data
● Whether or not to keep as univariate or multivariate (i.e., which features and number
of features)
● Outlier detection and removal
● Missing value imputation
16. Plotting the data
● There does appear to be an overall
increasing trend.
● There appears to be some differences
in the variance over time.
● There is seasonality (i.e., cycles) in the
data.
● There are outliers.
17. Look at Stationarity
● Most of the time-series model that we use assume the stationarity of time-series. This
assumption gives us some nice statistical properties thereby allowing us to use
various models for forecasting.
● To put it in layman terms, if we want to predict future using the past data, w should
assume that the data will follow the same trends and patterns as in the past. This
general statement holds for most training data and modeling tasks.
18. Look at Stationarity
Stationary Time-Series have following characteristics:
● Constant mean
● Constant variance
● Autocovariance doesn't depend on time
Sometimes in order to make time-series stationary we need to transform the data.
However, this transformation then calls into questions if this data is truly stationary and is
suited to be modeled using these techniques.
19. Dickey-Fuller Test
We can test Stationarity using moving
average statistics and Dickey-Fuller Test.
Following are the conditions for Hypothesis
testing using Dickey Fuller Test
● Null Hypothesis (H_0): time series is
not stationary
● Alternative Hypothesis (H_1): time
series is stationary
20. Handling Stationarity
It is common for a time-series to have Non stationary behaviour. Most common reason
behind non- stationary time-series are:
● Trend - mean is not constant over time .
● Seasonality - variance is not constant over time
There are ways to correct for trend and seasonality in order to make times-series
stationary.
21. What will happen if we don’t correct
stationarity?
Many things can happen:
● Variance can be mis-specified
● Model fit can be worse
● Not leveraging valuable time-dependent nature of data.
22. Eliminating Trend And Seasonality
● Transformation
○ Examples. Log, square root, etc.
● Smoothing
○ Examples. Weekly average, monthly average, rolling averages.
● Differencing
○ Examples. First-order differencing.
● Polynomial Fitting
○ Examples. Fit a regression model.
● Decomposition
25. For 44X72: Retail Trade and Food Services: U.S.
Total
● The low frequency plot shows us an estimate of the trend followed by the sales from 1992 to 2020.
The average increase rate is 12.795 billion dollars per year
● The high frequency plot shows us the seasonal changes in Retail sales. When I zoom into high
frequency we see a sales peak in December and also at around May and June and this pattern
occurs over every year. The seasonal change have a period of one year
● The middle frequency plot tells us about any long term changes is their is any. I can see that there is
a steady increase from 2004 to 2008, but from around 2nd quarter of 2008 retail sales begin to
decline and it is not until last quarter of 2009 it began to climb back. This shows the stock market
crash on Sept. 29, 2008.The decline in Retail Sales partly reflects the economic downturn.The same
is happening in the 1st quarter of 2020 due to COVID-19 outbreak which is still going on. The
decline in Retail Sales shows people refrain from spending and shows the lockdown situation of the
country
27. Why is statistical forecasting important (or at least, interesting)?
"Forecasting can take many forms—staring into crystal balls or bowls of tea
leaves, combining the opinions of experts, brainstorming, scenario
generation, what-if analysis, Monte Carlo simulation, solving equations that
are dictated by physical laws or economic theories—but statistical
forecasting, which is the main topic to be discussed here, is the art and
science of forecasting from data, with or without knowing in advance what
equation you should use."
Robert Nau, Principles and Risks of Forecasting
28. ARIMA Model (Autoregressive Moving Average)
We can use Arima model when we know there is dependence between values and we can
leverage that information to forecast.
Assumptions = The time-series is stationary
ARIMA depends on following 3 terms:
1. Number of AR (Auto-Regressive) terms (p).
2. Number of I (Integrated or Difference) terms (d).
3. Number of MA (Moving Average) terms (q).
29. ARIMA Model (Autoregressive Moving Average)
How do we determine p, d, and q? For this, we can use ACF and PACF plots
● Autocorrelation Function (ACF), correlation between the time series with a lagged version
of itself(Ex: Correlation of Y(t) with Y(t-1)).
● Partial Autocorrelation Function (PACF), Additional correlation explained by each
successive lagged terms.
How to interpret ACF and PACF plots?
● p - Lag value where the PACF chart crosses the upper confidence interval for the first time.
● q - Lag value where the ACF chart crosses the upper confidence interval for the first time.
30. ACF and PACF Functions:
How do we determine p, d, and q? For this, we can use ACF and PACF plots
● Autocorrelation Function (ACF), correlation between the time series with a lagged version
of itself(Ex: Correlation of Y(t) with Y(t-1)).
● Partial Autocorrelation Function (PACF), Additional correlation explained by each
successive lagged terms.
How to interpret ACF and PACF plots?
● p - Lag value where the PACF chart crosses the upper confidence interval for the first time.
● q - Lag value where the ACF chart crosses the upper confidence interval for the first time.
32. SARMA(Seasonal Autoregressive moving Average
● When we see a season in out time series we can use SARMA
● We can see a seasonal period of 12 on the retail sales data. A SARMA model with period
equal to 12 can be used.
● We use SARMA model on log transformed Data.
○ p = 1, q = 1 i.e. SARMA(p,q) ×(1,1)12 model was the best for total sales
35. Conclusion
● comparing with the model fitted on non transformed data the standard error has
significantly decreased
● The ACF doesn’t show significant autocorrelation among residuals.
● The red cure if from the model SARMA(p,q) ×(1,1)12 , which is very comparable to the
original data which is shown by black line.
● These plot shows residuals points are now better at following normal distribution.
● The transformation does deal with the problem of the heteroscedasticity.
36. Facebook Prophet package
Facebook Prophet is a tool that allows folks to forecast using additive or component
models relatively easily. It can also include things like:
● Day of week effects
● Day of year effects
● Holiday effects
● Trend trajectory
● Can do MCMC sampling
37.
38. Facebook Prophet package
● https://www.kaggle.com/landlord/us-retail-all-cat-covid-19-facebook-prophet
● Here is the complete forecasting of all catgiries using Facebook Prophet
39. Facebook Prophet package | Multiprocessing
● Adding multiprocessing to our code, Here we will launch a process for each time-serie
forecast, so we can run our run_prophet function in parallel while we do the map of
the list.
● https://www.kaggle.com/landlord/forecasting-multiple-time-series-using-prophet
● We could in the notebook see that using multiprocessing is a great way to forecasting
multiple time-series faster, in many problems multiprocessing could help to reduce
the execution time of our code.
40. LSTM for regression
● Unlike regression predictive modeling, time series also adds the complexity of a
sequence dependence among the input variables.
● A powerful type of neural network designed to handle sequence dependence is called
recurrent neural networks. The Long Short-Term Memory network or LSTM network
is a type of recurrent neural network used in deep learning because very large
architectures can be successfully trained.
41. LSTM for regression
One to One. Classic Neural Network.
One to Many. Classic Neural Network.
Neelabh Pant. Last accessed October 15, 2018. <https://blog.statsbot.co/time-series-prediction-using-recurrent-neural-networks-lstms-807fa6ca7f>.
42. 42
Neelabh Pant. Last accessed October 15, 2018. <https://blog.statsbot.co/time-series-prediction-using-recurrent-neural-networks-lstms-807fa6ca7f>.
Recurrent Neural Network (RNN) Long Short-Term Memory Network
(LSTM)
Able to capture longer-term dependencies
in a sequence.
LSTM for regression