Book Image

Hands-On Time Series Analysis with R

By : Rami Krispin
Book Image

Hands-On Time Series Analysis with R

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.
Table of Contents (14 chapters)

Forecasting Strategies

So far, we have mainly been focusing on the first two components of the time series analysis workflow—data preprocessing and descriptive analysis. Starting from this chapter, we will shift gear and move on to the third and last component of the analysis—the forecast. Before we dive into different forecasting models in the upcoming chapters, we will introduce the main elements of the forecasting workflow. This includes approaches for training a forecasting model, performance evaluation, and benchmark methods. This will provide you with a set of tools for designing and building a forecasting model according to the goal of the analysis.

This chapter covers the following topics:

  • Training and testing approaches for a forecasting model
  • Performance evaluation methods and error measurement matrices
  • Benchmark methods
  • Quantifying forecast uncertainty...