One of the limitations of the AR, MA, and ARMA models is that they cannot handle non-stationary time series data. Therefore, if the input series is non-stationary, a preprocessing step is required to transform the series from a non-stationary state into a stationary state. The ARIMA model provides a solution for this issue by adding the integrated process for the ARMA model. The Integrated (I) process is simply differencing the series with its lags, where the degree of the differencing is represented by the d parameter. The differencing process, as we saw previously, is one of the ways you can transform the methods of a series from non-stationary to stationary. For instance, Yt - Yt-1 represents the first differencing of the series, while (Yt - Yt-1) - (Yt-1 - Yt-2) represents the second differencing. We can generalize the differencing process with the following...
Hands-On Time Series Analysis with R
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Hands-On Time Series Analysis with R
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Overview of this book
Time-series analysis is the art of extracting meaningful insights from, and revealing patterns in, time-series data using statistical and data visualization approaches. These insights and patterns can then be utilized to explore past events and forecast future values in the series.
This book explores the basics of time-series analysis with R and lays the foundation you need to build forecasting models. You will learn how to preprocess raw time-series data and clean and manipulate data with packages such as stats, lubridate, xts, and zoo. You will analyze data using both descriptive statistics and rich data visualization tools in R including the TSstudio, plotly, and ggplot2 packages. The book then delves into traditional forecasting models such as time-series linear regression, exponential smoothing (Holt, Holt-Winter, and more) and Auto-Regressive Integrated Moving Average (ARIMA) models with the stats and forecast packages. You'll also work on advanced time-series regression models with machine learning algorithms such as random forest and Gradient Boosting Machine using the h2o package.
By the end of this book, you will have developed the skills necessary for exploring your data, identifying patterns, and building a forecasting model using various traditional and machine learning methods.
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Hands-On Time Series Analysis with R
Table of Contents (14 chapters)
Preface
Free Chapter
Introduction to Time Series Analysis and R
Working with Date and Time Objects
The Time Series Object
Working with zoo and xts Objects
Decomposition of Time Series Data
Seasonality Analysis
Correlation Analysis
Forecasting Strategies
Forecasting with Linear Regression
Forecasting with Exponential Smoothing Models
Forecasting with ARIMA Models
Forecasting with Machine Learning Models
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