So far, our focus in this chapter was mainly on the attributes of the date and time classes. Let's now connect the dots and see some useful applications of time series data. As introduced in Chapter 1, Introduction to Time Series Analysis and R, the main characteristic of time series data is its time index (or timestamp), an equally spaced time interval. The base package provides two pairs of functions, seq.Date and seq.POSIXt, to create a time index vector with Date or POSIX objects respectively. The main difference between the two functions (besides the class of the output) is the units of the time interval. It will make sense to use the seq.Date function to generate a time sequence with daily frequency or lower (for example, weekly, monthly, and so on) and as.POSIXt in other instances (for higher frequencies than daily, such as hourly, half...
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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.
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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