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Hands-On Time Series Analysis with R

By : Rami Krispin

3.8 (11)

Hands-On Time Series Analysis with R

3.8 (11)

By: Rami Krispin

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.

Preface

Preface

Who this book is for

What this book covers

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Introduction to Time Series Analysis and R

Introduction to Time Series Analysis and R

Technical requirements

Time series data

Historical background of time series analysis

Time series analysis

Getting started with R

A brief introduction to R

Working and manipulating data

Summary

Working with Date and Time Objects

Working with Date and Time Objects

Technical requirements

The date and time formats

Date and time objects in R

Creating a date or time index

Manipulation of date and time with the lubridate package

Summary

The Time Series Object

The Time Series Object

Technical requirement

The Natural Gas Consumption dataset

The attributes of the ts class

Data manipulation of ts objects

Visualizing ts and mts objects

Summary

Working with zoo and xts Objects

Working with zoo and xts Objects

Technical requirement

The zoo class

The xts class

Manipulating the zoo and xts objects

Plotting zoo and xts objects

xts, zoo, or ts – which one to use?

Summary

Decomposition of Time Series Data

Decomposition of Time Series Data

Technical requirement

The moving average function

The time series components

The additive versus the multiplicative model

The decomposition of time series

Seasonal adjustment

Summary

Seasonality Analysis

Seasonality Analysis

Technical requirement

Seasonality types

Seasonal analysis with descriptive statistics

Structural tools for seasonal analysis

Summary

Correlation Analysis

Correlation Analysis

Technical requirement

Correlation between two variables

Lags analysis

The autocorrelation function

The partial autocorrelation function

Lag plots

Causality analysis

Summary

Forecasting Strategies

Forecasting Strategies

Technical requirement

The forecasting workflow

Training approaches

Finalizing the forecast

Handling forecast uncertainty

Summary

Forecasting with Linear Regression

Forecasting with Linear Regression

Technical requirement

The linear regression

Forecasting with linear regression

Forecasting a series with multiseasonality components – a case study

Summary

Forecasting with Exponential Smoothing Models

Forecasting with Exponential Smoothing Models

Technical requirement

Forecasting with moving average models

Forecasting with exponential smoothing

Summary

Forecasting with ARIMA Models

Forecasting with ARIMA Models

Technical requirement

The stationary process

The AR process

The moving average process

The ARMA model

Forecasting AR, MA, and ARMA models

The ARIMA model

The seasonal ARIMA model

The auto.arima function

Linear regression with ARIMA errors

Summary

Forecasting with Machine Learning Models

Forecasting with Machine Learning Models

Technical requirement

Why and when should we use machine learning?

Why h2o?

Forecasting monthly vehicle sales in the US – a case study

Summary

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The moving average process

In some cases, the forecasting model is unable to capture all the series patterns, and therefore some information is left over in model residuals (or forecasting error) . The goal of the moving average process is to capture patterns in the residuals, if they exist, by modeling the relationship between Y_t, the error term, ∈_t, and the past q error terms of the models (for example, ). The structure of the MA process is fairly similar to the ones of the AR. The following equation defines an MA process with a q order:

The following terms are used in the preceding equation:

MA(q) is the notation for an MA process with q-order
represents the mean of the series
are white noise error terms
is the corresponding coefficient of
q defines the number of past error terms to be used in the equation

Like the AR process, the MA equation holds only if the...

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