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Название: Time Series Forecasting with R: A Beginner's Guide Автор: Arunachalam Rajagopal Издательство: Amazon.com Services LLC Год: 2019 Язык: английский Формат: pdf (true) Размер: 15.4 MB
This book offers the reader with basic concepts in R programming for time series forecasting. The tools covered are Simple Moving Average (SMA), Exponential Moving Average (EMA), HoltWinter’s model, Auto Regressive Integrated Moving Average (ARIMA), SARIMA (Seasonal ARIMA), and Dynamic Regression or ARIMAX.
The residuals analysis is an important aspect of time series forecasting and tools like qqplot, Cumulative periodogram (cpgram), and Box test have been used for this purpose throughout the book. Proper residual analysis will ensure model validity and accuracy of prediction.
Knowledge of Business Statistics and R programming is prerequisite for this book. This book offers the reader with basic concepts in R programming for time series forecasting. The tools covered are Simple Moving Average (SMA), Exponential Moving Average (EMA), HoltWinter’s model, Auto Regressive Integrated Moving Average (ARIMA), SARIMA (Seasonal ARIMA), and Dynamic Regression or ARIMAX.
The residuals analysis is an important aspect of time series forecasting and tools like qqplot, Cumulative periodogram (cpgram), and Box test have been used for this purpose throughout the book. Proper residual analysis will ensure model validity and accuracy of prediction.
Knowledge of Business Statistics and R programming is prerequisite for this book.
Table of Contents: 01 Simple Moving Average (SMA) 02 Exponential Moving Average (EMA) 03 Holtwinter’s Models without trend 04 Holtwinter’s Models with trend 05 Holtwinter’s Seasonal Models 06 ARIMA Model 07 Seasonal ARIMA (SARIMA) 08 ARIMAX / Dynamic Regression Annexure-I: Dataset Annexure-Ii: Reference and Bibliography
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Time Series Forecasting with R: A Beginner's Guide 
Название: Time Series Forecasting with R: A Beginner's Guide 
