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Hands-On Time Series Analysis with R

Hands-On Time Series Analysis with R

By : Rami Krispin
3.8 (11)
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Hands-On Time Series Analysis with R

Hands-On Time Series Analysis with R

3.8 (11)
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)
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Summary

The use of descriptive statistics and data visualization tools plays a pivotal role in the seasonality analysis of time series data. As we saw in this chapter, there is a close relationship between the frequency of the series and the type of seasonal patterns. A series with a lower frequency (such as monthly or quarterly) would potentially have a single dominant seasonal pattern. On the other hand, if the series frequency is higher, the probability is that multiple seasonal patterns exist in the series. This, of course, should help you to determine which tools or approaches to use in the analysis process. Last but not least, in some instances, you should consider removing exogenous factors (such as the series trend) to get a clear picture of the seasonal patterns and to avoid misleading results.

In the next chapter, we will focus on the correlation analysis of time series...

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