This document provides extra analysis methods for time series data as part of an assessment. It discusses using indexes to adjust for inflation, identifying additive and multiplicative trends, and analyzing deflated time series data using different trend models like linear and quadratic. The document explains how to graph raw data, isolate components, choose the best analysis method, and justify the trend model.
Analyzing and forecasting time series data ppt @ bec domsBabasab Patil
This document discusses forecasting time-series data using various models. It covers identifying components in time series, computing index numbers, smoothing-based and trend-based forecasting models, measuring forecast accuracy, and addressing autocorrelation. The key steps are developing models, identifying trends and seasonal components, computing forecasts, and comparing forecasts to actual data to evaluate model fit.
This chapter discusses time series analysis and forecasting. The key components are:
1. A time series contains data recorded over time and can be analyzed to identify trends and patterns that may continue in the future.
2. The components of a time series are secular trends, cyclical variation, seasonal variation, and irregular variation.
3. Moving averages and weighted moving averages can be used to smooth time series data and identify trends. Linear and nonlinear trend lines can also model trends in the data.
4. Seasonal indices identify seasonal patterns that repeat each year and can be used to deseasonalize time series data. Autocorrelation tests whether residuals are independent or correlated over time.
The document discusses different types of moving averages used for demand forecasting, including simple moving average, weighted moving average, and exponential weighted moving average. An exponential weighted moving average assigns greater weight and importance to more recent data points compared to older data. The exponential weighted moving average model was adapted by Holt and Winter to include a trend term that allows it to capture changes over time. The model calculates the weighted average using a smoothing factor lambda between 0 and 1, with larger lambda values giving more weight to recent data.
Chap19 time series-analysis_and_forecastingVishal Kukreja
Trend + Seasonality + Cyclical + Irregular
Multiplicative Model
X t = Trend × Seasonality × Cyclical × Irregular
This chapter discusses time-series analysis and forecasting methods. It covers computing and interpreting index numbers, testing for randomness, and identifying trend, seasonality, cyclical and irregular components in a time series. It also describes smoothing-based forecasting models like moving averages and exponential smoothing, as well as autoregressive and autoregressive integrated moving average models. The chapter aims to help readers analyze time-series data and develop forecasts.
Moving avg & method of least squareHassan Jalil
A quantitative method of forecasting or smoothing a time series by averaging each successive group (no. of observations) of data values.
Term MOVING is used because it is obtained by summing and averaging the values from a given no of periods, each time deleting the oldest value and adding a new value.
The document discusses the moving average method for forecasting or smoothing time series data. It explains that a moving average is calculated by averaging successive data points over a set time period, with old data dropped as new data is added. The document outlines how to calculate moving averages for both odd and even time periods. It also discusses the merits and limitations of the moving average method, and provides an example calculation.
This document discusses time series analysis and its key components. It begins by defining a time series as a sequence of data points measured over successive time periods. The four main components of a time series are identified as: 1) Trend - the long-term pattern of increase or decrease, 2) Seasonal variations - repeating patterns over 12 months, 3) Cyclical variations - fluctuations lasting more than a year, and 4) Irregular variations - unpredictable fluctuations. Two common methods for measuring trends are introduced as the moving average method and least squares method. Formulas and examples are provided for calculating trend values using these techniques.
Analyzing and forecasting time series data ppt @ bec domsBabasab Patil
This document discusses forecasting time-series data using various models. It covers identifying components in time series, computing index numbers, smoothing-based and trend-based forecasting models, measuring forecast accuracy, and addressing autocorrelation. The key steps are developing models, identifying trends and seasonal components, computing forecasts, and comparing forecasts to actual data to evaluate model fit.
This chapter discusses time series analysis and forecasting. The key components are:
1. A time series contains data recorded over time and can be analyzed to identify trends and patterns that may continue in the future.
2. The components of a time series are secular trends, cyclical variation, seasonal variation, and irregular variation.
3. Moving averages and weighted moving averages can be used to smooth time series data and identify trends. Linear and nonlinear trend lines can also model trends in the data.
4. Seasonal indices identify seasonal patterns that repeat each year and can be used to deseasonalize time series data. Autocorrelation tests whether residuals are independent or correlated over time.
The document discusses different types of moving averages used for demand forecasting, including simple moving average, weighted moving average, and exponential weighted moving average. An exponential weighted moving average assigns greater weight and importance to more recent data points compared to older data. The exponential weighted moving average model was adapted by Holt and Winter to include a trend term that allows it to capture changes over time. The model calculates the weighted average using a smoothing factor lambda between 0 and 1, with larger lambda values giving more weight to recent data.
Chap19 time series-analysis_and_forecastingVishal Kukreja
Trend + Seasonality + Cyclical + Irregular
Multiplicative Model
X t = Trend × Seasonality × Cyclical × Irregular
This chapter discusses time-series analysis and forecasting methods. It covers computing and interpreting index numbers, testing for randomness, and identifying trend, seasonality, cyclical and irregular components in a time series. It also describes smoothing-based forecasting models like moving averages and exponential smoothing, as well as autoregressive and autoregressive integrated moving average models. The chapter aims to help readers analyze time-series data and develop forecasts.
Moving avg & method of least squareHassan Jalil
A quantitative method of forecasting or smoothing a time series by averaging each successive group (no. of observations) of data values.
Term MOVING is used because it is obtained by summing and averaging the values from a given no of periods, each time deleting the oldest value and adding a new value.
The document discusses the moving average method for forecasting or smoothing time series data. It explains that a moving average is calculated by averaging successive data points over a set time period, with old data dropped as new data is added. The document outlines how to calculate moving averages for both odd and even time periods. It also discusses the merits and limitations of the moving average method, and provides an example calculation.
This document discusses time series analysis and its key components. It begins by defining a time series as a sequence of data points measured over successive time periods. The four main components of a time series are identified as: 1) Trend - the long-term pattern of increase or decrease, 2) Seasonal variations - repeating patterns over 12 months, 3) Cyclical variations - fluctuations lasting more than a year, and 4) Irregular variations - unpredictable fluctuations. Two common methods for measuring trends are introduced as the moving average method and least squares method. Formulas and examples are provided for calculating trend values using these techniques.
This document discusses different average-based forecasting methods including simple average, moving average, weighted average, and cumulative average. It provides examples and formulas for calculating forecasts for each method. The simple average method calculates the forecast as the average of all past data points. The moving average method uses the average of the most recent data points. The weighted average assigns different weights to each data point. The cumulative average adds the most recent actual value to the cumulative sum of weighted past increases.
This document discusses techniques for time-series analysis and forecasting including smoothing data using moving averages and exponential smoothing, fitting trend lines using linear, quadratic and exponential models, and using autoregressive models. It describes choosing the appropriate forecasting model by analyzing residuals and selecting the simplest model that adequately fits the data.
Time series analysis examines patterns in data over time. It relies on identifying trends, measuring past patterns to forecast the future, and decomposing time series into four main components: secular trends, cyclical movements, seasonal variations, and irregular variations. Secular trends represent long-term direction, while cyclical and seasonal variations have recurring patterns over different time scales. Various techniques can depict trends and identify variations, including freehand drawing, semi-averages, moving averages, least squares, and exponential smoothing.
Time series analysis involves analyzing data collected over time. A time series is a set of data points indexed in time order. The key components of a time series are trends, seasonality, cycles, and irregular variations. Trend refers to the long-term movement of a time series over time. Seasonality refers to periodic fluctuations that occur each year, such as higher sales in winter. Cyclical variations are longer term fluctuations in business cycles. Irregular variations are random, unpredictable fluctuations. Time series analysis is important for forecasting, economic analysis, and business planning. Common methods for analyzing time series components include moving averages, least squares regression, decomposition models, and harmonic analysis.
The document provides an overview of classical decomposition for time series analysis. It explains that classical decomposition can be used to isolate the trend, seasonal, and cyclical components of a time series. The document then describes the basic steps of classical decomposition, which include determining seasonal indexes, deseasonalizing the data, developing a trend-cyclical regression equation, and creating a forecast using trend data and seasonal indexes. An example applying these steps to sales data for a company is also presented.
The document discusses seasonal adjustment methods for time series forecasts. It defines seasonality and explains common causes of seasonal patterns. The main seasonal adjustment method described is a four-step process: 1) forecast demand values, 2) calculate demand/forecast ratios, 3) average ratios to determine seasonal indices, 4) adjust forecasts by multiplying them by seasonal indices. An example is provided to illustrate applying this method to quarterly widget demand data.
This document provides an overview of time series analysis and forecasting techniques. It discusses key concepts such as stationary and non-stationary time series, additive and multiplicative models, smoothing methods like moving averages and exponential smoothing, autoregressive (AR), moving average (MA) and autoregressive integrated moving average (ARIMA) models. The document uses examples to illustrate how to identify patterns in time series data and select appropriate models for description, explanation and forecasting of time series.
This document discusses various forecasting methods including:
- Calculating forecasts using moving averages, weighted moving averages, and exponential smoothing
- Choosing the appropriate forecasting model based on data availability, time horizon, required accuracy, and resources
- Comparing forecast accuracy using metrics like forecast error which measure the difference between actual and forecasted values
The document provides an overview of time series analysis, including definitions, components, and methods for measuring trends, seasonal variations, cyclical variations, and irregular variations in time series data. It discusses adjusting raw time series data, measuring linear and nonlinear trends, converting annual trends to monthly trends, and different methods for measuring seasonal, cyclical, and irregular variations, including indexes and averages. Examples are provided to illustrate calculating seasonal variations using the monthly average method.
This document discusses various techniques for time-series analysis and forecasting, including decomposition methods, smoothing methods like moving averages, exponential smoothing, and trend and autoregressive models. It covers identifying components like trend and seasonality, fitting linear, quadratic and exponential trend models, developing autoregressive models of different orders, and selecting the appropriate forecasting model based on residual analysis and model simplicity.
Time Series Analysis - 1 | Time Series in R | Time Series Forecasting | Data ...Simplilearn
This document discusses time series forecasting. It begins with an introduction to time series analysis and its components, including trend, seasonality, cyclicity, and irregularity. It then provides an example of using a moving average method to smooth and forecast quarterly car sales data over five years. The moving average helps extract the trend from the raw time series data by removing the seasonal and irregular components. This smoothed data can then be used to forecast future time periods.
This document discusses quantitative forecasting methods, including time series and causal models. It covers key time series components like trend, seasonality, and cycles. Three main time series methods are described: smoothing, trend projection, and trend projection adjusted for seasonal influence. Moving averages and exponential smoothing are explained as common techniques for forecasting stationary time series. The document also covers decomposing a time series into trend, seasonal, and irregular components. Regression methods are mentioned as another approach when a trend is present in the data.
This document defines time series and its components. A time series is a set of observations recorded over successive time intervals. It has four main components: trend, seasonality, cycles, and irregular variations. Trend refers to the overall increasing or decreasing tendency over time. Seasonality refers to predictable changes that occur around the same time each year. Cycles have periods longer than a year. Irregular variations are random fluctuations. The document also discusses methods for analyzing time series components including additive, multiplicative, and mixed models.
This document provides an overview of time series analysis and its key components. It discusses that a time series is a set of data measured at successive times joined together by time order. The main components of a time series are trends, seasonal variations, cyclical variations, and irregular variations. Time series analysis is important for business forecasting, understanding past behavior, and facilitating comparison. There are two main mathematical models used - the additive model which assumes data is the sum of its components, and the multiplicative model which assumes data is the product of its components. Decomposition of a time series involves discovering, measuring, and isolating these different components.
Demand forecasting by time series analysisSunny Gandhi
Demand is a buyer's willingness and ability to pay for a product or service. Demand forecasting estimates the quantity of a product that consumers will purchase. It is important for resource distribution, production planning, pricing decisions, and reducing business risk. Demand forecasting can be done at the micro, industry, or macro level. Common forecasting methods include time series analysis of historical sales data, market testing, and qualitative techniques like educated guesses. Accurate, plausible, simple, and durable demand forecasts are ideal.
This document provides an overview of forecasting methods for operations management. It defines forecasting and identifies key principles. Quantitative and qualitative forecasting methods are described, including time series models, causal models, and techniques for addressing trends, seasonality, and error measurement. Guidelines for selecting the appropriate forecasting method and software are also provided.
This document provides an overview of time series analysis and forecasting using neural networks. It discusses key concepts like time series components, smoothing methods, and applications. Examples are provided on using neural networks to forecast stock prices and economic time series. The agenda covers introduction to time series, importance, components, smoothing methods, applications, neural network issues, examples, and references.
Time series decomposition involves breaking down a time series into various components: trend, seasonality, and error/noise. There are different decomposition models such as additive and multiplicative. Smoothing methods like moving averages are used to estimate the trend-cycle component by reducing random variation. Box-Jenkins models combine autoregressive (AR) and moving average (MA) terms to model time series, and involve identification, estimation, and diagnostic stages.
1) To understand the underlying structure of Time Series represented by sequence of observations by breaking it down to its components.
2) To fit a mathematical model and proceed to forecast the future.
This document discusses time series analysis. It defines a time series as a collection of observations made sequentially over time. Examples include financial, scientific, demographic, and meteorological time series data. The document contrasts time series data with cross-sectional data. It also describes the components of a time series, including trends, seasonal variations, cyclical variations, and irregular/random variations. The purposes and uses of time series analysis are discussed, along with methods for decomposing and measuring trends in time series data.
The document discusses time series analysis and its key components. It defines a time series as a set of data points indexed (or listed or graphed) in time order. A time series collects readings of a variable at evenly-spaced periods of time. It notes that time is the independent variable while the data is the dependent variable. The document outlines the main components of time series as trends, seasonal variations, cyclical variations, and irregular variations. It provides examples and discusses methods for measuring each component, including free hand curve, semi-average, moving average, and least squares. The purposes and importance of time series analysis are also highlighted.
The document summarizes time series analysis conducted to forecast sales for an airline company over the next 12 months. Key steps included: 1) checking for volatility, non-stationarity and seasonality in the data; 2) creating training and test datasets; 3) building ARIMA models and selecting the best based on error metrics; 4) generating forecasts and calculating errors compared to actual data. The optimal model with AR=0 and MA=3 was chosen for final forecasting based on lowest MAPE.
This document discusses different average-based forecasting methods including simple average, moving average, weighted average, and cumulative average. It provides examples and formulas for calculating forecasts for each method. The simple average method calculates the forecast as the average of all past data points. The moving average method uses the average of the most recent data points. The weighted average assigns different weights to each data point. The cumulative average adds the most recent actual value to the cumulative sum of weighted past increases.
This document discusses techniques for time-series analysis and forecasting including smoothing data using moving averages and exponential smoothing, fitting trend lines using linear, quadratic and exponential models, and using autoregressive models. It describes choosing the appropriate forecasting model by analyzing residuals and selecting the simplest model that adequately fits the data.
Time series analysis examines patterns in data over time. It relies on identifying trends, measuring past patterns to forecast the future, and decomposing time series into four main components: secular trends, cyclical movements, seasonal variations, and irregular variations. Secular trends represent long-term direction, while cyclical and seasonal variations have recurring patterns over different time scales. Various techniques can depict trends and identify variations, including freehand drawing, semi-averages, moving averages, least squares, and exponential smoothing.
Time series analysis involves analyzing data collected over time. A time series is a set of data points indexed in time order. The key components of a time series are trends, seasonality, cycles, and irregular variations. Trend refers to the long-term movement of a time series over time. Seasonality refers to periodic fluctuations that occur each year, such as higher sales in winter. Cyclical variations are longer term fluctuations in business cycles. Irregular variations are random, unpredictable fluctuations. Time series analysis is important for forecasting, economic analysis, and business planning. Common methods for analyzing time series components include moving averages, least squares regression, decomposition models, and harmonic analysis.
The document provides an overview of classical decomposition for time series analysis. It explains that classical decomposition can be used to isolate the trend, seasonal, and cyclical components of a time series. The document then describes the basic steps of classical decomposition, which include determining seasonal indexes, deseasonalizing the data, developing a trend-cyclical regression equation, and creating a forecast using trend data and seasonal indexes. An example applying these steps to sales data for a company is also presented.
The document discusses seasonal adjustment methods for time series forecasts. It defines seasonality and explains common causes of seasonal patterns. The main seasonal adjustment method described is a four-step process: 1) forecast demand values, 2) calculate demand/forecast ratios, 3) average ratios to determine seasonal indices, 4) adjust forecasts by multiplying them by seasonal indices. An example is provided to illustrate applying this method to quarterly widget demand data.
This document provides an overview of time series analysis and forecasting techniques. It discusses key concepts such as stationary and non-stationary time series, additive and multiplicative models, smoothing methods like moving averages and exponential smoothing, autoregressive (AR), moving average (MA) and autoregressive integrated moving average (ARIMA) models. The document uses examples to illustrate how to identify patterns in time series data and select appropriate models for description, explanation and forecasting of time series.
This document discusses various forecasting methods including:
- Calculating forecasts using moving averages, weighted moving averages, and exponential smoothing
- Choosing the appropriate forecasting model based on data availability, time horizon, required accuracy, and resources
- Comparing forecast accuracy using metrics like forecast error which measure the difference between actual and forecasted values
The document provides an overview of time series analysis, including definitions, components, and methods for measuring trends, seasonal variations, cyclical variations, and irregular variations in time series data. It discusses adjusting raw time series data, measuring linear and nonlinear trends, converting annual trends to monthly trends, and different methods for measuring seasonal, cyclical, and irregular variations, including indexes and averages. Examples are provided to illustrate calculating seasonal variations using the monthly average method.
This document discusses various techniques for time-series analysis and forecasting, including decomposition methods, smoothing methods like moving averages, exponential smoothing, and trend and autoregressive models. It covers identifying components like trend and seasonality, fitting linear, quadratic and exponential trend models, developing autoregressive models of different orders, and selecting the appropriate forecasting model based on residual analysis and model simplicity.
Time Series Analysis - 1 | Time Series in R | Time Series Forecasting | Data ...Simplilearn
This document discusses time series forecasting. It begins with an introduction to time series analysis and its components, including trend, seasonality, cyclicity, and irregularity. It then provides an example of using a moving average method to smooth and forecast quarterly car sales data over five years. The moving average helps extract the trend from the raw time series data by removing the seasonal and irregular components. This smoothed data can then be used to forecast future time periods.
This document discusses quantitative forecasting methods, including time series and causal models. It covers key time series components like trend, seasonality, and cycles. Three main time series methods are described: smoothing, trend projection, and trend projection adjusted for seasonal influence. Moving averages and exponential smoothing are explained as common techniques for forecasting stationary time series. The document also covers decomposing a time series into trend, seasonal, and irregular components. Regression methods are mentioned as another approach when a trend is present in the data.
This document defines time series and its components. A time series is a set of observations recorded over successive time intervals. It has four main components: trend, seasonality, cycles, and irregular variations. Trend refers to the overall increasing or decreasing tendency over time. Seasonality refers to predictable changes that occur around the same time each year. Cycles have periods longer than a year. Irregular variations are random fluctuations. The document also discusses methods for analyzing time series components including additive, multiplicative, and mixed models.
This document provides an overview of time series analysis and its key components. It discusses that a time series is a set of data measured at successive times joined together by time order. The main components of a time series are trends, seasonal variations, cyclical variations, and irregular variations. Time series analysis is important for business forecasting, understanding past behavior, and facilitating comparison. There are two main mathematical models used - the additive model which assumes data is the sum of its components, and the multiplicative model which assumes data is the product of its components. Decomposition of a time series involves discovering, measuring, and isolating these different components.
Demand forecasting by time series analysisSunny Gandhi
Demand is a buyer's willingness and ability to pay for a product or service. Demand forecasting estimates the quantity of a product that consumers will purchase. It is important for resource distribution, production planning, pricing decisions, and reducing business risk. Demand forecasting can be done at the micro, industry, or macro level. Common forecasting methods include time series analysis of historical sales data, market testing, and qualitative techniques like educated guesses. Accurate, plausible, simple, and durable demand forecasts are ideal.
This document provides an overview of forecasting methods for operations management. It defines forecasting and identifies key principles. Quantitative and qualitative forecasting methods are described, including time series models, causal models, and techniques for addressing trends, seasonality, and error measurement. Guidelines for selecting the appropriate forecasting method and software are also provided.
This document provides an overview of time series analysis and forecasting using neural networks. It discusses key concepts like time series components, smoothing methods, and applications. Examples are provided on using neural networks to forecast stock prices and economic time series. The agenda covers introduction to time series, importance, components, smoothing methods, applications, neural network issues, examples, and references.
Time series decomposition involves breaking down a time series into various components: trend, seasonality, and error/noise. There are different decomposition models such as additive and multiplicative. Smoothing methods like moving averages are used to estimate the trend-cycle component by reducing random variation. Box-Jenkins models combine autoregressive (AR) and moving average (MA) terms to model time series, and involve identification, estimation, and diagnostic stages.
1) To understand the underlying structure of Time Series represented by sequence of observations by breaking it down to its components.
2) To fit a mathematical model and proceed to forecast the future.
This document discusses time series analysis. It defines a time series as a collection of observations made sequentially over time. Examples include financial, scientific, demographic, and meteorological time series data. The document contrasts time series data with cross-sectional data. It also describes the components of a time series, including trends, seasonal variations, cyclical variations, and irregular/random variations. The purposes and uses of time series analysis are discussed, along with methods for decomposing and measuring trends in time series data.
The document discusses time series analysis and its key components. It defines a time series as a set of data points indexed (or listed or graphed) in time order. A time series collects readings of a variable at evenly-spaced periods of time. It notes that time is the independent variable while the data is the dependent variable. The document outlines the main components of time series as trends, seasonal variations, cyclical variations, and irregular variations. It provides examples and discusses methods for measuring each component, including free hand curve, semi-average, moving average, and least squares. The purposes and importance of time series analysis are also highlighted.
The document summarizes time series analysis conducted to forecast sales for an airline company over the next 12 months. Key steps included: 1) checking for volatility, non-stationarity and seasonality in the data; 2) creating training and test datasets; 3) building ARIMA models and selecting the best based on error metrics; 4) generating forecasts and calculating errors compared to actual data. The optimal model with AR=0 and MA=3 was chosen for final forecasting based on lowest MAPE.
This document discusses time series analysis and its various components. It defines a time series as a set of observations made over time, usually at regular intervals. The key components of a time series are the secular trend, seasonal variation, cyclical variation, and irregular variation. Several methods are discussed for measuring and decomposing these components, including free hand curve, semi-average, moving average, and least squares methods. The importance of time series analysis for forecasting and planning is also highlighted. Time series analysis decomposes a time series into its various components to help understand the data better and make predictions.
Lucene Revolution 2016, Boston: Talk by Josef Adersberger (@adersberger, CTO at QAware).
Abstract: A lot of data is best represented as time series: Operational data, financial data, and even in data warehouses the dominant dimension is often time. We present Chronix, a time series database based on Apache Solr and Spark which is able to handle trillions of time series data points and perform interactive queries. Chronix Spark is open source software and battle-proven at a German car manufacturer and an international telco.
We demonstrate several use cases of Chronix from real-life. Afterwards we lift the curtain and deep-dive into the Chronix architecture esp. how we're using Solr to store time series data and how we've hooked up Solr with Spark. We provide some benchmarks showing how Chronix has outperformed other time series databases in both performance and storage-efficiency.
Chronix is open source under the Apache License (http://chronix.io).
The document provides an overview of a time series analysis and forecasting course. It discusses key topics that will be covered including descriptive statistics, correlation, regression, hypothesis testing, clustering, time series analysis and forecasting techniques like TCSI and ARIMA models. It notes that the presentation serves as class notes and contains informal high-level summaries intended to aid the author, and encourages readers to check the website for updated versions of the document.
Business models and financial modeling for startups.
Class material for the Founder Institute.
(cc) BY NC SA, Rodrigo SEPULVEDA SCHULZ
www.rodrigosepulveda.com
The document discusses uses of the Consumer Price Index (CPI) including calculating purchasing power and real income. Purchasing power decreases as prices rise, shown through examples comparing the CPI to income levels over several years. Real income is calculated by deflating current income by the CPI to account for inflation. Examples show how current income may rise nominally while real income decreases due to higher inflation. The final example demonstrates calculating real GDP and GNP by deflating current values using the CPI.
Assumptions: Check yo'self before you wreck yourselfErin Shellman
Predicting the future is hard and it requires a lot of assumptions, also known as beliefs, also known as faith. In “Assumptions: Check yo self, before you wreck yo self” we explore the consequences of beliefs when constructing predictive models. We’ll walk through the process of developing a demand forecast for Evo, a Seattle-based outdoor recreation retailer, and discuss how assumptions influence the behavior of your application and ultimately the decisions you make.
The document discusses index numbers, which are used as economic indicators to measure changes in economic activities such as prices, sales, exports, and production over time or between locations. There are two main types of index numbers: fixed base index numbers and chain base index numbers. Fixed base index numbers express current data as a percentage of a fixed base year, while chain base index numbers link indices over successive periods. The document provides an example to illustrate how to calculate a fixed base price index and discusses issues statisticians face when constructing index numbers such as choosing items, base years, and weighting formulas.
Business forecasting and timeseries analysis phpapp02MD ASADUZZAMAN
This document discusses time series analysis and forecasting. It defines forecasting as making predictions about the future based on past data and trends. Business forecasting estimates future sales, expenses, and profits. Time series analysis establishes relationships between variables over time. Key components of time series that influence trends include seasonal, cyclical, secular, and irregular variations. Common forecasting methods mentioned are regression analysis, exponential smoothing, and time series analysis. Measurement of trends can be done using techniques like least squares, moving averages, and semi-averages.
My presentation last night at the Founder Institute Brussels on "Business models, Revenues, Costs & Profits", ie. on financial modeling for startups.
(cc) BY NC SA, Rodrigo SEPÚLVEDA SCHULZ
www.rodrigosepulveda.com
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Things to Consider When Choosing a Website Developer for your Website | FODUUFODUU
Choosing the right website developer is crucial for your business. This article covers essential factors to consider, including experience, portfolio, technical skills, communication, pricing, reputation & reviews, cost and budget considerations and post-launch support. Make an informed decision to ensure your website meets your business goals.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
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- Practical examples and best practices to implement right away
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Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
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4. Deployment Using ArgoCD for Edge Devices
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5. Introduction to Apache Kafka and S3
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6. Viewing Kafka Messages in the Data Lake
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7. What is Prometheus?
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8. Monitoring Application Metrics with Prometheus
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9. What is Camel K?
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10. Configuring Camel K Integrations for Data Pipelines
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11. What is a Jupyter Notebook?
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12. Jupyter Notebooks with Code Examples
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Paper: https://doi.org/10.1007/978-3-031-61000-4_16
CAKE: Sharing Slices of Confidential Data on Blockchain
Analysis of time series
1. Analysis of Time Series
For AS90641
Part 2
Extra for Experts
September 2005 Created by Polly Stuart 1
2. Contents
• This resource is designed to suggest
some ways students could meet the
requirements of AS 90641.
• It shows some common practices in
New Zealand schools and suggests
other simplified statistical methods.
• The suggested methods do not
necessarily reflect practices of Statistics
New Zealand.
2
3. Aims
• This presentation (and the next) takes
you through some extra types of
analysis you could try for time series
data.
• It also makes suggestions for writing
your report
• You will need to open the spreadsheet:
Example sales.xls
• Choose the worksheet labeled
Clothing.
3
4. Beginnings
• You have already learned a basic
analysis of a time series and how to
isolate some components.
• We are now going to do a more
complex analysis.
• Before doing any analysis you need to:
– Graph the raw data
– Identify the components of the data
– Decide on the best method of
analysis. 4
5. Look at : the trend
the seasonal component
the irregular component
C l o t hi ng and so f t g o o d s sal es
$(million)
550
500
450
400
350
300
2500
Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
5
6. Step 1: Using Indexes
Indexes show how prices have changed over time.
They show the percentage increase in prices since
a base period. The index for the base period is
usually 1000.
An index of 1150 shows that prices have increased
15 percent since the base period.
You can use indexes to ‘deflate’ time series data
which contains dollar values.
Statistics New Zealand indexes include:
Consumers Price Index Labour Cost Index
Food Price Index Farm Expenses Price Index 6
7. Consumers Price Index
• The Consumers Price Index (CPI)
measures the change in prices of a
specific basket of goods and services in
New Zealand.
• For retail sales of clothing this is an
appropriate index to use as clothing is
included in the ‘basket’ of goods priced.
• Open the CPI worksheet and copy the
series into the next column of the clothing
worksheet.
Look at the CPI data. Which is the base period? How
do you know? 7
8. If the value of sales from clothing shops are
increasing over time there several possible
reasons:
• Prices have increased because of inflation
• The number of people in the population is growing
so there are more possible customers needing
clothes
• Sales are actually increasing because people are
buying more clothing
• Something else?
To help find out if total sales are increasing
because of inflation we can turn the sales into
constant 1999 dollars using the value of the CPI
for each year. 8
9. Constant dollars
The present base period for the Consumers
Price Index (CPI) is 1999.
Assume that the CPI now is 1150.
In 1999, $100 could buy the same amount as:
1150
100 $115 can buy now
1000
Now, $100 can buy the same amount as:
1000
100 $86.96 could buy in 1999
1150
9
10. Calculate your deflated value
We will
use
constant
1999
dollars
Use this
for the
formula to
rest of
calculate
this
the value in
exercise.
constant
1999
dollars. 10
11. Step 2: Deciding on an appropriate
model
• Some data follows an additive model
where:
Data value = trend + seasonal +
irregular
• Other data follows a multiplicative
model where:
Data value = trend x seasonal x
irregular
11
12. Additive
When the size of the
Series for which an additive series is
seasonal appropriate
250
component stays 200
about the same as 150
the trend changes, 100
then an additive 50
0
method is usually Mar 1991 Mar 1992 Mar 1993 Mar 1994
best. Original series
Trend series
12
13. Multiplicative
Series for which a multiplicative model is appropriate
When the size of
300
the seasonal 250
component 200
150
increases as the 100
trend increases, 50
then a 0
Mar 1991 Mar 1992 Mar 1993 Mar 1994
multiplicative Original series
method may be Trend series
better.
13
14. Look again at the graph below
• Which model seems more suitable?
In the previous PowerPoint we used an additive
model and we will do this also for this data
(An example of using a multiplicative model is
given at the end of the third presentation).
C l o t hi ng a nd s o f t g o o d s r e t a i l t r a d e
$million
550
500
450
400
350
300
250
0Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
14
15. Step 3: Analyse the data
• Do the spreadsheet analysis as far as
calculating the seasonally adjusted
data.
• Use the constant dollar values for your
analysis.
15
17. Step 4: Describe and justify your
model for the trend
• Try some different models for the
moving average.
• Decide which one will give a sensible
forecast.
17
18. Trend
Describe what you can see.
y = -0.0864x + 381.6
Clothing and softgoods sales
$(m illion)
500 Clothing
1999
450 dollars
Estimated
400 trend
350 Linear
(Estimated
300 trend)
250
0
Mar Mar Mar Mar Mar Mar Mar
1991 1993 1995 1997 1999 2001 2003
Does this linear trend model look sensible?
18
19. • Many trends cannot be modelled by a single
straight line
• A quadratic model may be tempting…
y = 0.1097x 2 - 5.572x + 431.66
Clothing and softgoods sales
$(m illion)
500 Clothing
1999
450 dollars
Estimated
400 trend
350 Poly.
(Estimated
300 trend)
2500
Mar Mar Mar Mar Mar Mar Mar
1991 1993 1995 1997 1999 2001 2003
But is it realistic?
19
20. • Remember the shape of a parabola.
• Do you think that sales (in constant dollars)
are going to grow at that rate?
y = 0.1097x 2 - 5.572x + 431.66
Clothing and softgoods sales
$(m illion)
600 Clothing
550 1999
500 dollars
Estimated
450 trend
400
Poly.
350 (Estimated
300 trend)
2500
Mar Mar Mar Mar Mar Mar Mar
1991 1993 1995 1997 1999 2001 2003
20
21. • An option is to use a linear model over the
trend at the end of the series.
• This is likely to give the most realistic forecast.
Clothing and softgoods sales from 1998
y = 4.3368x + 335.87
$(million)
500 Clothing
1999
450 dollars
400 Estimated
350 trend
300
Linear
250
0 (Estimated
trend)
Mar Mar Mar Mar Mar Mar
1998 1999 2000 2001 2002 2003
21
22. Step 5: Describing the seasonal
component
• A graph can help you to see the patterns more
clearly.
22
23. Seasonal sales patterns
$(m illion)
50
0
Mar 1991 Mar 1995 Mar 1999 Mar 2003
-50
Describe the patterns you can see.
You can also identify amounts easily from the
graph.
23
24. Step 6: Analysing the irregular
component
• There is always random variation in a
time series, the irregular component.
• When a very unusual event happens it
may cause a spike in the data, called an
outlier.
• This can distort the trend and seasonal
component values.
• The larger the spike the more distortion.
• It is useful to calculate the irregular
component and look for outliers. 24
25. Subtract the values in the ‘Seasonal’
column from the ‘Seasonal and Irregular’
column. A graph is often useful.
25
26. Outliers
Highlight the date and irregular columns for
the graph.
Irregular Com ponent
$ m i l l i on 19 9 9
15
10
5
0
Mar 1991 Mar 1995 Mar 1999 Mar 2003
-5
-10
Both the pattern of the irregular component
and any extreme values are worth
commenting on. 26
27. This is not the end!
Continue the analysis and
write a report on retail
clothing sales.
Some ideas are given in the
next presentation,
Reporting.
27