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)

Training approaches

One of the core elements of the forecasting workflow is the model training process. The quality of the model's training will have a direct impact on the forecast output. The main goals of this process are as follows:

  • Formalize the relationship of the series with other factors, such as seasonal and trend patterns, correlation with past lags, and external variables in a predictive manner
  • Tune the model parameters (when applicable)
  • The model is scalable on new data, or in other words, avoids overfitting

As we mentioned previously, prior to the training process, the series is split into training and testing partitions, where the model is being trained on the training partition and tested on the testing partition. These partitions must be in chronological order, regardless of the training approach that has been used. The main reason for this is that most...