This document discusses stochastic methods in hydrology, specifically Markov transition matrices and cumulative distribution functions. It describes how to calculate daily monsoon rainfall using a Markov chain model with four rainfall classes. The initial condition and transition probabilities are given. It also discusses stationary time series, linear stochastic models including moving averages, autoregressive models and autoregressive moving average models. Double moving averages are presented to remove trends and improve forecasts.
This document discusses regression analysis and its application in hydrology. It begins by defining regression as a statistical technique used to determine the functional relationship between two variables. Simple linear regression finds the best fit linear equation to describe the relationship between a dependent and independent variable. Regression can be used to predict outcomes, describe relationships, and control for variables. The document provides examples of applying regression to predict erosion based on wave height data. It explains how to calculate the regression equation and error term.
This document discusses correlation and statistical methods for examining the relationship between two variables. It defines correlation and describes how correlation can indicate the direction, strength, and significance of a relationship. Different types of correlation are described, including simple, multiple, partial, and total correlation. Methods for calculating and interpreting the correlation coefficient are provided along with examples of exploring relationships between hydrological variables.
This document discusses trend analysis of time series data. It defines time series as measurements of a variable taken at regular intervals over time. Time series can show trends, seasonal variations, cyclical variations, and irregular variations. Trend analysis determines if there is a significant increasing or decreasing trend in the data over time. Linear regression and non-parametric Mann-Kendall tests are common methods used to test for trends and estimate their magnitude. The selection of an appropriate trend analysis method depends on characteristics of the water resources data such as distributions, outliers, and missing values.
This document discusses statistical hydrology and summarizing data. It describes defining problems, collecting relevant data through sampling techniques, and assessing data quality before analysis. Statistical hydrology involves collecting and analyzing variable, limited water resources data to make decisions and scientific discoveries. Descriptive statistics are used to summarize datasets while inferential statistics enable inferences about unknown aspects.
The document discusses various statistical hypothesis tests that can be used to analyze hydrological data, including the t-test and ANOVA. It provides examples of how to set up null and alternative hypotheses, calculate relevant statistics like t-statistics and F-statistics, and make decisions about whether to reject the null hypothesis based on comparing these statistics to critical values. One example analyzes groundwater depth data from three catchments using ANOVA to test if depths differ between catchments.
This document discusses probability distributions and their applications in statistical hydrology. It begins by explaining discrete and continuous random variables and their probability functions. It then covers several specific probability distributions including binomial, Poisson, normal, lognormal, gamma, exponential and Gumbel distributions. Examples are provided to illustrate how these distributions can be used to calculate probabilities of hydrologic events like floods or rainfall.
This document discusses multiple linear regression techniques. It begins by explaining that multiple linear regression is used to predict a dependent variable from a set of independent variables. It then provides details on assumptions that must be satisfied, how to identify and handle outliers, and the steps involved in performing multiple linear regression analysis. Examples are also provided to illustrate key concepts.
This document discusses statistical methods for simple linear regression including tests of significance for the slope and intercept. It introduces alternative regression methods such as the Kendall-Theil robust line that can be used when the assumptions of ordinary least squares regression are not met, such as when the residuals are not normally distributed. An example demonstrates how to calculate the Kendall-Theil robust line and test its significance.
This document discusses regression analysis and its application in hydrology. It begins by defining regression as a statistical technique used to determine the functional relationship between two variables. Simple linear regression finds the best fit linear equation to describe the relationship between a dependent and independent variable. Regression can be used to predict outcomes, describe relationships, and control for variables. The document provides examples of applying regression to predict erosion based on wave height data. It explains how to calculate the regression equation and error term.
This document discusses correlation and statistical methods for examining the relationship between two variables. It defines correlation and describes how correlation can indicate the direction, strength, and significance of a relationship. Different types of correlation are described, including simple, multiple, partial, and total correlation. Methods for calculating and interpreting the correlation coefficient are provided along with examples of exploring relationships between hydrological variables.
This document discusses trend analysis of time series data. It defines time series as measurements of a variable taken at regular intervals over time. Time series can show trends, seasonal variations, cyclical variations, and irregular variations. Trend analysis determines if there is a significant increasing or decreasing trend in the data over time. Linear regression and non-parametric Mann-Kendall tests are common methods used to test for trends and estimate their magnitude. The selection of an appropriate trend analysis method depends on characteristics of the water resources data such as distributions, outliers, and missing values.
This document discusses statistical hydrology and summarizing data. It describes defining problems, collecting relevant data through sampling techniques, and assessing data quality before analysis. Statistical hydrology involves collecting and analyzing variable, limited water resources data to make decisions and scientific discoveries. Descriptive statistics are used to summarize datasets while inferential statistics enable inferences about unknown aspects.
The document discusses various statistical hypothesis tests that can be used to analyze hydrological data, including the t-test and ANOVA. It provides examples of how to set up null and alternative hypotheses, calculate relevant statistics like t-statistics and F-statistics, and make decisions about whether to reject the null hypothesis based on comparing these statistics to critical values. One example analyzes groundwater depth data from three catchments using ANOVA to test if depths differ between catchments.
This document discusses probability distributions and their applications in statistical hydrology. It begins by explaining discrete and continuous random variables and their probability functions. It then covers several specific probability distributions including binomial, Poisson, normal, lognormal, gamma, exponential and Gumbel distributions. Examples are provided to illustrate how these distributions can be used to calculate probabilities of hydrologic events like floods or rainfall.
This document discusses multiple linear regression techniques. It begins by explaining that multiple linear regression is used to predict a dependent variable from a set of independent variables. It then provides details on assumptions that must be satisfied, how to identify and handle outliers, and the steps involved in performing multiple linear regression analysis. Examples are also provided to illustrate key concepts.
This document discusses statistical methods for simple linear regression including tests of significance for the slope and intercept. It introduces alternative regression methods such as the Kendall-Theil robust line that can be used when the assumptions of ordinary least squares regression are not met, such as when the residuals are not normally distributed. An example demonstrates how to calculate the Kendall-Theil robust line and test its significance.
The document discusses concepts related to statistical analysis of hydrological data, including measures of skewness, kurtosis, outliers, and the common characteristics of water resources data. Skewness measures asymmetry in a distribution, while kurtosis measures peakedness. Outliers are identified using methods like Chauvenet's criterion, Grubbs' test, and Dixon's Q test. Water resources data commonly has a lower bound of zero, outliers, non-normal distributions, positive skewness, seasonal patterns, and positive autocorrelation between consecutive observations.
Accelerating the production of safety summary and clinical safety reports - a...Steffan Stringer
This document discusses automating the production of safety summaries and clinical study reports using LaTeX, R, and source control. It proposes a workflow where clinical data is transformed into CDISC SDTM/ADaM formats using R, and reports are generated as reproducible documents combining LaTeX documentation with R code and output. This approach aims to reduce errors, accelerate delivery times, and allow easier collaboration between physicians, data managers, programmers and writers. The key benefits are presented as producing clinical reports as reproducible code and establishing a fully integrated process for analysis and reporting.
This document provides guidance on using regression analysis to validate hydrological data. It discusses using simple linear regression to establish relationships between variables like rainfall and runoff. Key steps covered include estimating regression coefficients to minimize the error variance, measuring the goodness of fit using the coefficient of determination, and examining residuals over time and versus other variables to evaluate changes in the rainfall-runoff relationship. The overall aim is to detect errors in discharge data by comparing observed and computed runoff derived from regression models.
This document provides information about quality management tools and techniques. It discusses a quality management institute database that was created to track clinical data for a sepsis initiative. It describes how the database tracked various metrics and underwent iterative improvements based on data analysis. Over 8,000 cases of sepsis were eventually entered into the database. Common quality management tools are also defined, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms.
This document provides an overview of key components and activities involved in air quality management systems. It describes common air quality management activities like goal setting, control strategies, modeling, assessment, legislation/regulation, compliance, and monitoring. The document also lists several quality management tools that can be used for air quality management, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, histograms, and their purposes. Links to additional air quality management resources are also provided.
The document discusses quality assurance management tools and strategies. It provides descriptions and examples of 5 commonly used quality management tools: check sheets, control charts, Pareto charts, scatter plots, and Ishikawa diagrams. Each tool is explained in 1-2 paragraphs detailing what it is used for and how it works. Examples are given for control charts, Pareto charts, and scatter plots. The tools can help identify issues, determine causes of problems, and monitor quality over time.
This document provides an overview of quotes on quality management and lists several quality management tools including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. It also lists additional topics related to quality management such as quality management systems, courses, techniques, standards, policies, and strategies. The document contains information on defining and using several common quality management tools.
This document provides guidance on using social media for lead generation. It discusses social selling and prospecting by engaging with prospects through thoughtful content until they are ready to buy. The workbook teaches fundamentals of listening to social media conversations to generate leads beyond just monitoring keywords. It identifies the quickest ways to find prospects on LinkedIn, Twitter, and Google+ and provides exercises to engage with prospects and share relevant content on these platforms.
The document discusses measures of dispersion such as variance, standard deviation, and the coefficient of variation. It defines variance as the average squared deviation from the mean and standard deviation as the positive square root of the variance. The coefficient of variation measures relative dispersion by dividing the standard deviation by the mean. It is unit-free and allows for comparison across distributions. The document also covers Chebyshev's inequality and how it relates to the proportion of data within a given number of standard deviations from the mean.
Il corso fornisce e rende operativi sulle funzionalità di base e di comune interesse di Microsoft Powerpoint.
Dare una informativa sulle funzionalità più evolute di Powerpoint e fornire una chiara visione delle aree di
applicabilità di Powerpoint. Al termine del corso il candidato sarà in grado di utilizzare e organizzare e gestire Powerpoint.
Sheldon Jessup attended a 3-hour web course on 3D Visualization Techniques Using ArcGIS on August 7, 2016. The course provided 3 hours of training in 3D visualization techniques using ArcGIS software. Sheldon Jessup has completed the training course.
This document defines key vocabulary terms related to relations and functions such as domain, range, and discrete vs continuous relations. It provides examples of relations and uses the vertical line test to determine if they are functions. It also discusses evaluating functions by finding the output of a function given an input.
Il corso intende fornire le basi per introdurre in azienda la Gestione Totale per la Qualità, e per impostare i conseguenti piani/interventi di miglioramento, comprendendo la differenza tra il modello di gestione tradizionale ed il modello TQM e identificando i principali “salti culturali” richiesti e le resistenze al cambiamento da superare.
L’obiettivo di questo corso è fornire ai partecipanti la preparazione necessaria per selezionare il processo di implementazione appropriato per soluzioni aziendali caratterizzate da fault tolerance ed elevata disponibilità o da un elevato numero di utenti.
The Middlesex County Medical Reserve Corps trains its diverse members both individually and in groups in public health areas like prevention efforts and health literacy, emergency response areas such as CPR and triage, and often provides continuing education units. The corps consists of health professionals and community volunteers who are leaders in taking action.
This document provides an overview of topics to be covered in a 3-week professional engineering exam review session on hydrology and hydraulics. It will cover key aspects of hydrology including the hydrologic cycle, precipitation, runoff analysis using the Curve Number method, and peak discharge calculations. Hydraulics topics will include flow through common structures like weirs, orifices, and pipes. Example problems will be worked through for each major topic to illustrate concepts and calculations. Attendees are advised to review references and practice additional example problems.
The Vietnam National Mekong Committee conducted a Mekong Dam Study, the results of which were presented at the Greater Mekong Forum on Water, Food and Energy in Phnom Penh on Oct. 21, 2015. This presentation overviews their Modelling for the study.
This document summarizes Agrobacterium-mediated plant transformation. It describes how the soil bacterium Agrobacterium tumefaciens causes crown gall disease in plants by transferring oncogenic T-DNA from its Ti plasmid into the plant genome. Scientists have exploited this natural process to develop transformation systems where they insert new genes between the border sequences of disarmed Ti plasmids, allowing transfer of the recombinant T-DNA into plant cells. While effective in dicots, transformation of monocots proved more difficult due to their limited regeneration ability, though biolistic methods using microprojectile bombardment have succeeded in some important crop species.
The document discusses the Coastal Regulation Zone (CRZ) notification in India. Some key points:
- The CRZ extends 500 meters landward from the high tide line and includes the intertidal zone between the low and high tide lines.
- The CRZ is divided into 4 categories - I, II, III and IV - depending on the ecological sensitivity of the area.
- Category I areas have the highest level of protection due to their ecological importance. They include mangroves, coral reefs, parks and wildlife habitats.
- The 2011 CRZ notification revised the 1991 version to provide more uniform regulations while ensuring livelihoods and sustainable development.
Mpc 006 - 02-01 product moment coefficient of correlationVasant Kothari
1.2 Correlation: Meaning and Interpretation
1.2.1 Scatter Diagram: Graphical Presentation of Relationship
1.2.2 Correlation: Linear and Non-Linear Relationship
1.2.3 Direction of Correlation: Positive and Negative
1.2.4 Correlation: The Strength of Relationship
1.2.5 Measurements of Correlation
1.2.6 Correlation and Causality
1.3 Pearson’s Product Moment Coefficient of Correlation
1.3.1 Variance and Covariance: Building Blocks of Correlations
1.3.2 Equations for Pearson’s Product Moment Coefficient of Correlation
1.3.3 Numerical Example
1.3.4 Significance Testing of Pearson’s Correlation Coefficient
1.3.5 Adjusted r
1.3.6 Assumptions for Significance Testing
1.3.7 Ramifications in the Interpretation of Pearson’s r
1.3.8 Restricted Range
1.4 Unreliability of Measurement
1.4.1 Outliers
1.4.2 Curvilinearity
1.5 Using Raw Score Method for Calculating r
1.5.1 Formulas for Raw Score
1.5.2 Solved Numerical for Raw Score Formula
The document discusses concepts related to statistical analysis of hydrological data, including measures of skewness, kurtosis, outliers, and the common characteristics of water resources data. Skewness measures asymmetry in a distribution, while kurtosis measures peakedness. Outliers are identified using methods like Chauvenet's criterion, Grubbs' test, and Dixon's Q test. Water resources data commonly has a lower bound of zero, outliers, non-normal distributions, positive skewness, seasonal patterns, and positive autocorrelation between consecutive observations.
Accelerating the production of safety summary and clinical safety reports - a...Steffan Stringer
This document discusses automating the production of safety summaries and clinical study reports using LaTeX, R, and source control. It proposes a workflow where clinical data is transformed into CDISC SDTM/ADaM formats using R, and reports are generated as reproducible documents combining LaTeX documentation with R code and output. This approach aims to reduce errors, accelerate delivery times, and allow easier collaboration between physicians, data managers, programmers and writers. The key benefits are presented as producing clinical reports as reproducible code and establishing a fully integrated process for analysis and reporting.
This document provides guidance on using regression analysis to validate hydrological data. It discusses using simple linear regression to establish relationships between variables like rainfall and runoff. Key steps covered include estimating regression coefficients to minimize the error variance, measuring the goodness of fit using the coefficient of determination, and examining residuals over time and versus other variables to evaluate changes in the rainfall-runoff relationship. The overall aim is to detect errors in discharge data by comparing observed and computed runoff derived from regression models.
This document provides information about quality management tools and techniques. It discusses a quality management institute database that was created to track clinical data for a sepsis initiative. It describes how the database tracked various metrics and underwent iterative improvements based on data analysis. Over 8,000 cases of sepsis were eventually entered into the database. Common quality management tools are also defined, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms.
This document provides an overview of key components and activities involved in air quality management systems. It describes common air quality management activities like goal setting, control strategies, modeling, assessment, legislation/regulation, compliance, and monitoring. The document also lists several quality management tools that can be used for air quality management, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, histograms, and their purposes. Links to additional air quality management resources are also provided.
The document discusses quality assurance management tools and strategies. It provides descriptions and examples of 5 commonly used quality management tools: check sheets, control charts, Pareto charts, scatter plots, and Ishikawa diagrams. Each tool is explained in 1-2 paragraphs detailing what it is used for and how it works. Examples are given for control charts, Pareto charts, and scatter plots. The tools can help identify issues, determine causes of problems, and monitor quality over time.
This document provides an overview of quotes on quality management and lists several quality management tools including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. It also lists additional topics related to quality management such as quality management systems, courses, techniques, standards, policies, and strategies. The document contains information on defining and using several common quality management tools.
This document provides guidance on using social media for lead generation. It discusses social selling and prospecting by engaging with prospects through thoughtful content until they are ready to buy. The workbook teaches fundamentals of listening to social media conversations to generate leads beyond just monitoring keywords. It identifies the quickest ways to find prospects on LinkedIn, Twitter, and Google+ and provides exercises to engage with prospects and share relevant content on these platforms.
The document discusses measures of dispersion such as variance, standard deviation, and the coefficient of variation. It defines variance as the average squared deviation from the mean and standard deviation as the positive square root of the variance. The coefficient of variation measures relative dispersion by dividing the standard deviation by the mean. It is unit-free and allows for comparison across distributions. The document also covers Chebyshev's inequality and how it relates to the proportion of data within a given number of standard deviations from the mean.
Il corso fornisce e rende operativi sulle funzionalità di base e di comune interesse di Microsoft Powerpoint.
Dare una informativa sulle funzionalità più evolute di Powerpoint e fornire una chiara visione delle aree di
applicabilità di Powerpoint. Al termine del corso il candidato sarà in grado di utilizzare e organizzare e gestire Powerpoint.
Sheldon Jessup attended a 3-hour web course on 3D Visualization Techniques Using ArcGIS on August 7, 2016. The course provided 3 hours of training in 3D visualization techniques using ArcGIS software. Sheldon Jessup has completed the training course.
This document defines key vocabulary terms related to relations and functions such as domain, range, and discrete vs continuous relations. It provides examples of relations and uses the vertical line test to determine if they are functions. It also discusses evaluating functions by finding the output of a function given an input.
Il corso intende fornire le basi per introdurre in azienda la Gestione Totale per la Qualità, e per impostare i conseguenti piani/interventi di miglioramento, comprendendo la differenza tra il modello di gestione tradizionale ed il modello TQM e identificando i principali “salti culturali” richiesti e le resistenze al cambiamento da superare.
L’obiettivo di questo corso è fornire ai partecipanti la preparazione necessaria per selezionare il processo di implementazione appropriato per soluzioni aziendali caratterizzate da fault tolerance ed elevata disponibilità o da un elevato numero di utenti.
The Middlesex County Medical Reserve Corps trains its diverse members both individually and in groups in public health areas like prevention efforts and health literacy, emergency response areas such as CPR and triage, and often provides continuing education units. The corps consists of health professionals and community volunteers who are leaders in taking action.
This document provides an overview of topics to be covered in a 3-week professional engineering exam review session on hydrology and hydraulics. It will cover key aspects of hydrology including the hydrologic cycle, precipitation, runoff analysis using the Curve Number method, and peak discharge calculations. Hydraulics topics will include flow through common structures like weirs, orifices, and pipes. Example problems will be worked through for each major topic to illustrate concepts and calculations. Attendees are advised to review references and practice additional example problems.
The Vietnam National Mekong Committee conducted a Mekong Dam Study, the results of which were presented at the Greater Mekong Forum on Water, Food and Energy in Phnom Penh on Oct. 21, 2015. This presentation overviews their Modelling for the study.
This document summarizes Agrobacterium-mediated plant transformation. It describes how the soil bacterium Agrobacterium tumefaciens causes crown gall disease in plants by transferring oncogenic T-DNA from its Ti plasmid into the plant genome. Scientists have exploited this natural process to develop transformation systems where they insert new genes between the border sequences of disarmed Ti plasmids, allowing transfer of the recombinant T-DNA into plant cells. While effective in dicots, transformation of monocots proved more difficult due to their limited regeneration ability, though biolistic methods using microprojectile bombardment have succeeded in some important crop species.
The document discusses the Coastal Regulation Zone (CRZ) notification in India. Some key points:
- The CRZ extends 500 meters landward from the high tide line and includes the intertidal zone between the low and high tide lines.
- The CRZ is divided into 4 categories - I, II, III and IV - depending on the ecological sensitivity of the area.
- Category I areas have the highest level of protection due to their ecological importance. They include mangroves, coral reefs, parks and wildlife habitats.
- The 2011 CRZ notification revised the 1991 version to provide more uniform regulations while ensuring livelihoods and sustainable development.
Mpc 006 - 02-01 product moment coefficient of correlationVasant Kothari
1.2 Correlation: Meaning and Interpretation
1.2.1 Scatter Diagram: Graphical Presentation of Relationship
1.2.2 Correlation: Linear and Non-Linear Relationship
1.2.3 Direction of Correlation: Positive and Negative
1.2.4 Correlation: The Strength of Relationship
1.2.5 Measurements of Correlation
1.2.6 Correlation and Causality
1.3 Pearson’s Product Moment Coefficient of Correlation
1.3.1 Variance and Covariance: Building Blocks of Correlations
1.3.2 Equations for Pearson’s Product Moment Coefficient of Correlation
1.3.3 Numerical Example
1.3.4 Significance Testing of Pearson’s Correlation Coefficient
1.3.5 Adjusted r
1.3.6 Assumptions for Significance Testing
1.3.7 Ramifications in the Interpretation of Pearson’s r
1.3.8 Restricted Range
1.4 Unreliability of Measurement
1.4.1 Outliers
1.4.2 Curvilinearity
1.5 Using Raw Score Method for Calculating r
1.5.1 Formulas for Raw Score
1.5.2 Solved Numerical for Raw Score Formula
Big data is being used to predict weather patterns and avoid flight delays related to weather. Researchers at the University of Michigan gathered over 10 years of hourly weather observations and flight data, applying advanced analytics to identify patterns. This allows airlines to anticipate delays from storms and offer alternatives to passengers earlier. The system classifies weather data to predict future conditions and minimize impacts to travel.
A COMPARATIVE STUDY OF DIFFERENT INTEGRATED MULTIPLE CRITERIA DECISION MAKING...Shankha Goswami
This document summarizes a research study comparing multiple criteria decision making (MCDM) methodologies and their applications. The objectives are to select the best option among alternatives using hybrid MCDM methods, validate results by comparing outputs, and study application areas. Methodologies compared include AHP, TOPSIS, SAW, PROMETHEE, and AHP-fuzzy. As a case study, different laptop models are evaluated and ranked using the methods. Results show Model 5 is the best laptop based on criteria weights. The document concludes the methodologies provide the same rankings and validation, and future work could consider more criteria/applications and other MCDM tools.
Visualizing and Forecasting Stocks Using Machine LearningIRJET Journal
This document discusses using hidden Markov models to visualize and forecast stock prices using machine learning. It presents the results of using hidden Markov models and support vector regression to predict stock prices for Tata Motors, Reliance, and YES Bank. The hidden Markov model achieved prediction accuracies greater than 80% for short-term forecasts, outperforming support vector regression as measured by mean absolute percentage error. While both methods tracked stock price patterns well, the hidden Markov model was found to be more sensitive to changes in stock price. The document concludes the hidden Markov model is effective for stock price prediction and minimizing the impact of factor selection compared to other methods.
450595389ITLS5200INDIVIDUALASSIGNMENTS22015Leonard Ong
- The document discusses applying time series analysis to forecast container throughput at Sydney Ports terminals.
- It examines BITRE data on total container throughput from 2006-2014, finding seasonality. Nine forecasting methods are applied including simple and weighted moving averages, exponential smoothing, and methods accounting for trends and seasonality.
- The Holt-Winters method produced the most accurate forecast for September 2014 throughput, based on having the lowest errors according to MAD, MSE, and MAPE calculations.
ANALYSIS OF PRODUCTION PERFORMANCE OF TAMILNADU NEWSPRINT AND PAPERS LTD – C...Editor IJCATR
Every day, Tamilnadu Newsprint and Papers Ltd managers must make decisions about Production delivery without
knowing what will happen in the future. Forecasts enable them to anticipate the future and plan, many forecasting methods are
available to Tamilnadu Newsprint and Papers Ltd managers for planning, to estimate future demand or any other issues at hand.
However, for any type of forecast to bring about later success, it must follow a step-by-step process comprising five major steps: 1)
goal of the forecast and the identification of resources for conducting it; 2) time horizon; 3) selection of a forecasting technique; 4)
conducting and completing the forecast; and 5) monitoring the accuracy of the forecast. Accordingly Linear Regression method is a
widely used to predict this kind of demand. In this paper, we forecast the Production of Papers in TamilNadu Newsprint and Papers
Ltd from the past 15 years of Production using the Linear Regression method
Time Series Weather Forecasting Techniques: Literature SurveyIRJET Journal
This document summarizes various time series forecasting techniques discussed in literature, including ARIMA, Prophet, and LSTM models. It reviews their applications in weather forecasting, analyzing COVID-19 data, real estate prices, bitcoin values, and more. The key techniques are compared based on their forecasting accuracy on different datasets. ARIMA is generally good at capturing trends but requires stationary data, while Prophet and LSTM can handle non-stationary data and seasonal effects better. Prophet achieved 91% accuracy on a COVID dataset, outperforming ARIMA. LSTM achieved 76% accuracy for rainfall forecasting. The document concludes different approaches are still being developed to address the unique challenges of weather data forecasting.
The document discusses data streaming in IoT and big data analytics. It begins with an introduction to data streaming and the need for streaming techniques due to the complexity of analyzing large volumes of IoT data. It then covers the data streaming processing paradigm, including continuous queries, stateless and stateful operators, and windows. Challenges and research questions in data streaming are also discussed, such as distributed deployment, parallelism, and fault tolerance. The document concludes that data streaming is well-suited for real-time analysis of IoT data due to its ability to perform online, parallel and distributed processing.
This presentation provides an overview of a flood and rainfall prediction system. The system aims to increase awareness and reduce loss by allowing users to search rainfall ranges and flood histories in different areas. It uses machine learning models like artificial neural networks trained on historical rainfall and flood data to provide real-time flood predictions and early warnings. The system has features like fast performance, hazard mapping, and update capabilities. It faces challenges in data collection, model selection, and accuracy improvement with limited data.
This document provides an introduction to optimization. It discusses constrained and unconstrained optimization problems and provides examples of optimization applications in various fields including supermarket chains, telecommunications, banking and finance, manufacturing, healthcare, machine learning, and industry 4.0. The goal of optimization is to obtain the most desirable outcome at minimum cost by applying mathematical techniques to real-world problems.
This document is a thesis submitted by Gurminder Bharani to Symbiosis Institute of Geoinformatics in partial fulfillment of an M.Sc. degree. The thesis is titled "Automated Drought Analysis with Python and Machine Learning". It describes using Python and machine learning techniques to automate the analysis of drought conditions from satellite and other climate data sources. The thesis includes chapters on the literature review, study area, methodology, results, discussion, conclusion, and references.
Using Data Integration to Deliver Intelligence to Anyone, AnywhereSafe Software
Data integration makes it possible to deliver intelligence and keep decision makers, first responders, and civilians informed. For over 20 years, FME has been trusted by federal governments to move data from nearly any source to the target destination, while saving time and budget resources.
With FME, federal governments can deliver open data, improve emergency & disaster response, enhance land management, turn public safety and defense into actionable results, and integrate & deliver location intelligence.
This document presents a summary of a project to develop a computer program called the Emergency Decision Support System (EDSS) to calculate projected radiation doses in off-site areas from accidents at nuclear power plants. The EDSS will use the Lagrangian particle model and input from the CALMET model of the site's meteorology to predict dose dispersion up to 25 km from the source. It is designed to assist decision-makers at the Chashma Nuclear Power Plant site in Pakistan in responding to nuclear emergencies through tabulated and visualized dose data. Future work will focus on incorporating source term estimation and reverse modeling capabilities into the EDSS.
Students at the University of Michigan are researching how to use big data to help predict weather patterns and avoid flight delays related to weather. They analyzed 10 years of hourly weather data, which is a huge dataset, to understand similarities in past weather that could help predict future weather. This predictive analysis using big data has the potential to help airlines be cautious of bad weather in advance and prevent delays or cancellations. The goal is to apply big data computing methods to the large weather dataset to solve the social issue of frequent flight delays and cancellations due to unexpected weather.
This report describes work implementing functionality for impact sound insulation data in the NRC soundPATHS web application. It provides an overview of concepts related to sound transmission, including the differences between airborne and impact sound transmission. It then describes adding features to import and apply impact insulation data in the existing data entry tool, as well as safety checks and stored procedures implemented.
This document provides an overview of streaming analytics, including definitions, common use cases, and key concepts like streaming engines, processing models, and guarantees. It also provides examples of analyzing data streams using Apache Spark Structured Streaming, Apache Flink, and Kafka Streams APIs. Code snippets demonstrate windowing, triggers, and working with event-time.
This document summarizes a student project on queuing theory using the M/M/1 model. It includes an acknowledgements section thanking those who guided the project. It then covers queuing theory concepts like characteristics, assumptions, and formulas. The document describes simulating arrival and service data and comparing results to theoretical values. It finds the simulated values match theory. It also analyzes how changing arrival and service rates could improve the system's performance.
This document provides a disaster recovery plan for Interpharm to recover critical services within 4 hours in the event of a total network outage. It includes a business impact analysis identifying critical IT functions and their maximum tolerable outage times. The plan establishes an emergency recovery site with dedicated internet, servers, workstations, and backups of network devices that can be accessed if the primary site is lost. Key steps in disaster recovery implementation include reporting issues, declaring a disaster, contacting personnel, obtaining documentation, restoring backups, and testing the recovery.
The CPM (Critical Path Method) model is a deterministic approach used for project planning and scheduling. It involves identifying all tasks, determining their duration and sequence, and analyzing which path has no float or slack time between tasks. This critical path determines the minimum time needed to complete the project. The CPM makes assumptions that the critical path will not change, activity times are independent and known, and the project completion time is normally distributed. The key steps of CPM include specifying activities, establishing sequences, building a network diagram, estimating activity times, identifying the critical path, and updating the critical path diagram. The overall goal of CPM is to complete the project in the shortest time possible through techniques like fast tracking and crashing the critical
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
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1. MAL1303: STATISTICAL HYDROLOGY
Stochastic Methods in Hydrology
Dr. Shamsuddin Shahid
Department of Hydraulics and Hydrology
Faculty of Civil Engineering, Universiti Teknologi Malaysia
Room No.: M46-332; Phone: 07-5531624; Mobile: 0182051586
Email: sshahid@utm.my
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2. Markov Transition Matrix
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3. For four class, there will be four cumulative distribution functions.
Cumulative distribution functions for each class is calculated as,
Fj (x) = P [next day rainfall < x; when rainfall today belongs to class Cj].
For Example,
FR5(x) = P [next day rainfall < x; when rainfall today belongs to class R5].
Cumulative Distribution Functions
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4. Fj (x) = P [next day rainfall < x;
when rainfall today belongs to class Cj].
For Example:
FR5(x) = P [next day rainfall < x;
when rainfall today belongs to class R5].
P [next day rainfall < 5] = 2
P [next day rainfall < 4] = 2
P [next day rainfall < 3] = 2
P [next day rainfall < 2] = 1
P [next day rainfall < 1] = 1
Rainfall
10
5
1
6
23
4
3
2
0
20
5
2
3
0
4
3
1
0
Cumulative Distribution Functions
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5. FR5(x) = P [next day rainfall < x;
when rainfall today belongs to class R5].
P [next day rainfall < 5] = 2
P [next day rainfall < 4] = 2
P [next day rainfall < 3] = 2
P [next day rainfall < 2] = 1
P [next day rainfall < 1] = 1
Cumulative Distribution Functions
Find the distribution
and distribution
parameters.
Consider, we found
distribution is
exponential,
FR5(x) = exp (-)
Where,
= 0.105
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6. Calculate Daily Monsoon Rainfall
First, we need to define the initial condition.
Consider, Initial condition
R5 --- R10 --- R20 --- R>20
(1/4) (1/4) (1/4) (1/4)
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7. Calculate Daily Monsoon Rainfall
(1/4) (1/4) (1/4) (1/4)
[0.25 0.25 0.25 0.25] X
0.39 0.21 0.27 0.14
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8. R5 R10 R20 R>20
0.39 0.21 0.27 0.14
FR5(x) = exp (-x)
Where,
= 0.105
Cumulative Distribution,
1 - exp (-x)
Rainfall in Day1 (x) =
0.39 = 1 -0.105exp(-0.105x)
Calculate Daily Monsoon Rainfall
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9. Calculate Daily Monsoon Rainfall
0.39 0.21 0.27 0.14 X
0.41 0.24 0.24 0.11
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10. General equation is,
u(n) = u Pn
Or
u(n) = u(n-1) P
Calculate Daily Monsoon Rainfall
0.39 0.21 0.27 0.14 X
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11. Stochastic refers to systems whose behaviour is intrinsically non-
deterministic. A stochastic process is one whose behavior is non-
deterministic, in that a system's subsequent state is determined
both by the process's predictable actions and by a random element.
Stochastic hydrology is mainly concerned with the assessment of
uncertainty in model predictions
Stochastic Process
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12. Application of Stochastic Process in Hydrology
Stochastic hydrology is an essential base of water resources
systems analysis, due to the inherent randomness of the input,
and consequently of the results.
Stochastic process is applied for forecasting of hydrological
phenomena such as, flood, droughts, etc.
Stochastic process is applied for forecasting rainfall, river
discharge, etc.
Stochastic hydrology is very important in decision-making
process regarding the planning and management of water
systems.
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13. A stationary time series is one whose statistical properties such as
mean, variance, autocorrelation, etc. are all constant over time.
Most statistical forecasting methods are based on the assumption
that the time series can be rendered approximately stationary through
the use of mathematical transformations.
A stationarized series is relatively easy to predict: you simply predict
that its statistical properties will be the same in the future as they
have been in the past.
Stationary Time Series
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14. Linear Stochastic Models
1. Moving Average (MA)
2. Auto Regression (AR)
3. Auto Regressive Moving Average (ARMA)
4. Auto Regressive Integrated Moving Average (ARIMA)
Stochastic Models
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15. Moving Average
The concept underlying moving average is that the k most recent
time periods is a good predictor of the current and next period
values.
The process is called moving averages because each average is
calculated by dropping the oldest observation and including the
next observation.
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16. • The moving average removes some of the non-randomness in the data.
• Therefore, the moving average merely smooth the fluctuations in the
data.
• The moving average technique is a good forecasting approach to use if
the data is stationary.
k
Y....YYYY
F kttttt
t
1321
1
Where, Ft+1 is the forecast for period t+1, and
Yt is the actual value of period t
Moving Average
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18. Moving Average
48.0
59.0
69.3 53.5
68.0 64.2
67.3 68.7
59.0 67.7
51.0 63.1
41.0 55.0
30.7 46.0
31.0 35.8
30.7 30.8
39.0 30.8
49.0 34.9
61.0 44.0
68.3 55.0
68.0 64.7
65.3 68.2
59.0 66.7
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19. Moving Average
k
Y....YYYY
L kttttt'
t
1321
Moving Average, Lt
48.0
59.0 53.5
69.3 64.2
68.0 68.7
67.3 67.7
59.0 63.1
51.0 55.0
41.0 46.0
30.7 35.8
31.0 30.8
30.7 30.8
39.0 34.9
49.0 44.0
61.0 55.0
68.3 64.7
68.0 68.2
65.3 66.7
59.0 62.1
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20. Double Moving Average
k
L....LLLL
L
'
kt
'
t
'
t
'
t
'
t"
t
1321
48.0
59.0 53.5
69.3 64.2 58.8
68.0 68.7 66.4
67.3 67.7 68.2
59.0 63.1 65.4
51.0 55.0 59.1
41.0 46.0 50.5
30.7 35.8 40.9
31.0 30.8 33.3
30.7 30.8 30.8
39.0 34.9 32.8
49.0 44.0 39.4
61.0 55.0 49.5
68.3 64.7 59.8
68.0 68.2 66.4
65.3 66.7 67.4
59.0 62.1 64.4
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21. Double Moving Average
Difference between Actual value and first moving average is called Lag1.
Second Lag or Lag2 can be calculated as,
/
k
t
/
t LLlag
2
12
For example, if first moving average is calculate for K=3, then
/
t
/
t
/
t
/
t LLLLlag 1
2
132
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22. Data Forecast Error MA Lag1 Lag2
10.0
12.0 11.0
14.0 11.0 3.0 13.0 1.0 2.0
16.0 13.0 3.0 15.0 1.0 2.0
18.0 15.0 3.0 17.0 1.0 2.0
20.0 17.0 3.0 19.0 1.0 2.0
22.0 19.0 3.0 21.0 1.0 2.0
24.0 21.0 3.0 23.0 1.0 2.0
26.0 23.0 3.0 25.0 1.0 2.0
28.0 25.0 3.0 27.0 1.0 2.0
30.0 27.0 3.0 29.0 1.0 2.0
32.0 29.0 3.0 31.0 1.0 2.0
34.0 31.0 3.0 33.0 1.0 2.0
36.0 33.0 3.0 35.0 1.0 2.0
38.0 35.0 3.0 37.0 1.0 2.0
40.0 37.0 3.0 39.0 1.0 2.0
42.0 39.0 3.0 41.0 1.0 2.0
44.0 41.0 3.0 43.0 1.0 2.0
Double Moving Average
For constant trend, the
error is contact.
Double moving average is
used to remove the
constant trend.
Error is the sum of lag1
and lag2.
Therefore,
211 laglagMAFt
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23. Double Moving Average: Forecasting
Double moving average can be used for forecasting using following
formulas:
mbaF ttt 1
Where,
//
t
/
tt
//
t
/
t
/
tt
LL
k
b
and
]LL[La
1
2
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24. Data L'(t) L"(t) Lag2 Trend Forecast Error
10.0
12.0 11.0
14.0 13.0 12.0 1.0 2.0
16.0 15.0 14.0 1.0 2.0 16.0 0.0
18.0 17.0 16.0 1.0 2.0 18.0 0.0
20.0 19.0 18.0 1.0 2.0 20.0 0.0
22.0 21.0 20.0 1.0 2.0 22.0 0.0
24.0 23.0 22.0 1.0 2.0 24.0 0.0
26.0 25.0 24.0 1.0 2.0 26.0 0.0
28.0 27.0 26.0 1.0 2.0 28.0 0.0
30.0 29.0 28.0 1.0 2.0 30.0 0.0
32.0 31.0 30.0 1.0 2.0 32.0 0.0
34.0 33.0 32.0 1.0 2.0 34.0 0.0
36.0 35.0 34.0 1.0 2.0 36.0 0.0
38.0 37.0 36.0 1.0 2.0 38.0 0.0
40.0 39.0 38.0 1.0 2.0 40.0 0.0
42.0 41.0 40.0 1.0 2.0 42.0 0.0
44.0 43.0 42.0 1.0 2.0 44.0 0.0
Double Moving Average: Forecasting
//
t
/
tt
//
t
/
t
/
tt
LL
k
b
and
]LL[La
1
2
ttt baF 1
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25. Data L'(t) L"(t) Lag2 Trend Forecast
10
11
13
16
18
21
22 15.9
25 18.0
27 20.3
28 22.4
30 24.4
31 26.3
35 28.3 22.2 6.1 2.0
36 30.3 24.3 6.0 2.0 36.4
38 32.1 26.3 5.8 1.9 38.3
39 33.9 28.2 5.6 1.9 39.9
43 36.0 30.2 5.8 1.9 41.3
44 38.0 32.1 5.9 2.0 43.8
47 40.3 34.1 6.2 2.1 45.8
48 42.1 36.1 6.0 2.0 48.5
50 44.1 38.1 6.1 2.0 50.2
51 46.0 40.1 5.9 2.0 52.2
54 48.1 42.1 6.0 2.0 53.9
Double Moving Average: Forecasting
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26. Autocorrelation
Autocorrelation is the correlation of a series with itself. This is
unlike cross-correlation, which is the correlation of two different
series.
Autocorrelation is useful for finding repeating patterns in a time
series, such as determining the presence of a periodic signal or
cycle.
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27. Autocorrelation
t = 1
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28. Autocorrelation
t = 3
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29. Autocorrelation
t = 1; r = 0.9
t = 3; r = 0.5
t = 5; r = 0.0
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30. Autocorrelation
t = 0 or t=20; r = 1.0
t = 15; r = 0.0
t = 10; r = -1.0
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32. Autocorrelation
Test for significance of autocorrelation coefficient:
Where,
t is the lag
r is the autocorrelation coefficient at that lag, and
n is the number of observation
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33. Autocorrelation
Hypothesis Testing:
H0: r is attributable to randomness. No cycle present in the time
series.
HA: A cycle present in the time series.
If the calculated value of Z > 1.96
Null hypothesis rejected
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34. Overall Significance: Ljung-Box Statistics
Null hypothesis: At least one correlation is non-zero.
Test for significance of autocorrelation coefficient:
Where,
h is the number of autocorrelation coefficients being tested.
r is the autocorrelation coefficient at that lag, and
n is the number of observation
If, Qh > 2 (0.05, h), Null hypothesis is rejected.
h
k
kh rkn)n(nQ
1
21
2
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39.
h
k
kh rkn)n(nQ
1
21
2
Auto Regression (AR)
h = 9.
r is the autocorrelation coefficient at that lag
n = 23
Null hypothesis: At least one correlation is non-zero.
Qh = 42.59
2 (0.05, h) = 16.92
Qh > 2 , Reject H0
At least one correlation
is non-zero.
lag-1 -0.34061
lag-2 -0.01525
lag-3 -0.14931
lag-4 -0.15717
lag-5 0.0482
lag-6 -0.30402
lag-7 0.940332
lag-8 -0.28836
lag-9 -0.10714
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40. Auto Regression (AR)
Confidence interval of correlogram,
Z(/2)/n
Z at p = 0.05 = 1.96
n = 23
Z(/2)/n = 0.408
Lag = 7
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41. Auto Regression (AR)
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42. Auto Regression (AR)
Yt = 0.778Yt-7 + 2.337
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46. Confidence interval of correlogram,
Z(/2)/n
Z at p = 0.05 = 1.96
n = 73
Z(/2)/n = 0.2294
Lag = 1, 2, 3, 5, 6, 7, 8, 9
Auto Regression (AR)
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48. Auto Regression (AR)
98877665543322110
tttttttt
t
YbYbYbYbYbYbYbYbb
Y
48
59.00365
69.32472
68
67.31207
58.98174
50.97471
40.99635
30.67528
31
30.68793
39.01826
49.02529
61.00365
68.32472
68
65.31207
58.98174
48.97471
-
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49. Autocorrelation
Limitations of Autocorrelation:
1. The observations must be regularly spaced through time.
2. Any linear trend in the data should be removed in advance. Linear
trends will cause a gradual decline in peaks on the
autocorrelogram with increasing lag.
3. In order for there to be sufficient comparisons in the calculation of
the coefficient, the rules of thumb are: (a) there should be at least
50 observations in the time series; and (b) the lag should not
exceed n/4
4. Significantly high r values at small lags may not reflect cyclicity but
just smoothness in the data.
5. Although significantly negative Z values are possible, these are
not important as they correspond to negative autocorrelation,
themselves due to peak-trough correspondences in the data;
these will inevitably occur in association with high positive(peak-
peak; trough-trough) autocorrelations and offer no additional
information.
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50. Autoregressive Moving-Average (ARMA) models form a class of
linear time series models.
ARMA is a combination of AR and MA
Autoregressive Moving-Average (ARMA) =
Auto-Regression (AR) + Moving Average (MA)
Auto Regressive Moving Average (ARMA)
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51. eYb.......YbYbYbbY LktLtLtLtt 83322110
LktkLtLtLtt eb.......ebebebbY 3322110
Auto Regressive Moving Average (ARMA)
Auto-Regression (AR)
The error term is calculated from Moving average.
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53. Data L'(t) L"(t) Lag2
10
11
9
15
9
10
11
11 10.75
12 11
10 10.875
16 11.75 1 0.766
10 11.125 0.125 -0.152
9 11.125 0.25 -0.305
10 11.125 -0.625 -0.152
10 11 -0.125 -0.152
13 11.25 0.125 0.307
10 11 -0.125 -0.152
17 11.875 0.875 0.919
9 11 -0.25 -0.305
8 10.75 -0.25 -0.458
10 10.875 -1 -0.152
10 10.875 -0.125 -0.152
13 11.25 0.5 0.307
Auto Regressive Moving Average (ARMA)
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54. Auto Regressive Moving Average (ARMA)
Data Lag
16 0.766
10 -0.152
9 -0.305
10 -0.152
10 -0.152
13 0.307
10 -0.152
17 0.919
9 -0.305
8 -0.458
10 -0.152
10 -0.152
13 0.307
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59. Non-stationary Time Series
The models are applicable to stationary time series only.
If the parameters like autocorrelation varies with time, these
models can not be used
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60. Auto Regressive Integrated Moving Average (ARIMA)
Most naturally-occurring time series in hydrology are not at all stationary
(at least when plotted in their original units). Instead they exhibit various
kinds of trends, cycles, and seasonal patterns.
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61. • The best strategy may not be to try to directly predict the level of the series
at each period.
• Instead, it may be better to try to predict the change that occurs from one
period to the next (i.e., the quantity Y(t)-Y(t-1)).
• In other words, it may be helpful to look at the first difference of the series,
to see if a predictable pattern can be discerned there.
• For practical purposes, it is just as good to predict the next change as to
predict the next level of the series, since the predicted change can always be
added to the current level to yield a predicted level
Differencing
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62. The seasonal difference of a time series is the series of changes from one
season to the next. For monthly data, in which there are 4 seasons, the
seasonal difference of Y at period t is Y(t)-Y(t-4).
The first difference of the seasonal difference of a monthly time series Y at
period t is equal to (Y(t) - Y(t-4)) - (Y(t-1) - Y(t-5). Equivalently, it is equal to
(Y(t) - Y(t-1)) - (Y(t-4) - Y(t-5)).
Seasonal Differencing
11/23/2015 Shamsuddin Shahid, FKA, UTM
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63. Several approaches are there to identify, measure and remote the trend
and seasonal components of the time series data.
One of the easiest and most common method is differencing.
The first difference,
Y’t = Yt – Yt-1
is one way to ca capture and remove the effect of the trend.
Seasonal Differencing
11/23/2015 Shamsuddin Shahid, FKA, UTM
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64. ARIMA models are, in theory, the most general class of models for
forecasting a time series which can be stationarized by
transformations such as differencing and logging.
A ARIMA model is classified as an ARIMA(p,d,q) model, where:
p is the number of autoregressive terms,
d is the number of nonseasonal differences, and
q is the number of lagged forecast errors in the prediction equation.
ARIMA(1,1,1)
ARIMA(1,0,1)
ARIMA(2,1,2)
Auto Regressive Integrated Moving Average (ARIMA)
11/23/2015 Shamsuddin Shahid, FKA, UTM
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65. Auto Regressive Integrated Moving Average (ARIMA)
Box-Jenkins methodology.
1. Model Selection
2. Parameter Estimations
3. Model Checking
Many cases it is a iterative processes.
11/23/2015 Shamsuddin Shahid, FKA, UTM
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