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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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Seasonal adjustment

Seasonal adjustment is the process of removing the seasonal fluctuation from a series. The use of this process is popular in the field of economic research, as it provides a better overview of series changes over time. A common example is the Gross Domestic Production (GDP) index, one of the main indicators of economic health. This indicator has a strong seasonal pattern, as the majority of production in most sectors is affected by seasonal events through the calendar year, such as weather (for example, the agriculture sector) or holidays (for example, the retail and airline sectors). As a result, in a calendar year, some calendar quarters (for example, the first quarter of the year) will be higher (or lower) than others.

The US GDP is a good example as, historically, the growth in the first quarter is the lowest and highest in the second quarter, due to those...

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