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)

Why and when should we use machine learning?

In recent years, the use of machine learning (ML) models has become popular and accessible due to significant improvement in standard computation power. This led to a new world of methods and approaches for regression and classifications models. The process of creating time series forecasting with ML models follows the same process we used in Chapter 9, Forecasting with Linear Regression, with the linear regression model.

Before we start diving into the details, it is important to caveat the use of ML models in the context of time series forecasting:

  • Cost: The use of ML models is typically more expensive than typical regression models, both in computing power and time.
  • Accuracy: The ML model's performance is highly dependent on the quality (that is, strong casualty relationship with the dependent variable) of the predictors....