 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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