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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.