Build efficient forecasting models using traditional time series models and machine learning algorithms.
Key Features
Perform time series analysis and forecasting using R packages such as Forecast and h2o
Develop models and find patterns to create visualizations using the TSstudio and plotly packages
Master statistics and implement time-series methods using examples mentioned
Book Description
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 foundations 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 and extract meaningful information from it using both descriptive statistics and rich data visualization tools in R such as the TSstudio, plotly, and ggplot2 packages. The later section of the book 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 cover 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 the skills needed to explore your data, identify patterns, and build a forecasting model using various traditional and machine learning methods.
What you will learn
Visualize time series data and derive better insights
Explore auto-correlation and master statistical techniques
Use time series analysis tools from the stats, TSstudio, and forecast packages
Explore and identify seasonal and correlation patterns
Work with different time series formats in R
Explore time series models such as ARIMA, Holt-Winters, and more
Evaluate high-performance forecasting solutions
Who this book is for
Hands-On Time Series Analysis with R is ideal for data analysts, data scientists, and all R developers who are looking to perform time series analysis to predict outcomes effectively. A basic knowledge of statistics is required; some knowledge in R is expected, but not mandatory.