Methods for Time Series Forecasting: with Applications in R

This book provides a comparison of different forecasting methods with special implementations for different time series data and evaluates the results of forecasting experiments. It is important for the selected forecasting method to give more accurate results and smaller forecast errors. Also, the coding and the implementation processes have an effect in choosing a forecasting method. The first chapter explains forecasting, the second chapter gives information about the forecasting process, the third chapter gives examples of the forecasting methods used in the literature. The explanation of forecasting methods begins with the fourth chapter with the explanation of the so called “naive method”. The fifth chapter explains time series regression methods, the sixth chapter presents exponential smoothing methods, the seventh chapter gives information about the (S)ARIMA method. The eight chapter explains the implementation of these forecasting methods written on R program.The experiments evaluating these forecasting methods are introduced and explained in the ninth chapter. Finally, a general overview and comparison of the methods is presented.