finelybook
Hands-On Time Series Analysis with R: Perform time series analysis and forecasting using R
by 作者: Rami Krispin
pages 页数: 448 pages
ISBN-10 书号: 1788629159
ISBN-13 书号: 9781788629157
05 x 2.57 x 23.5 cm
Publisher Finelybook 出版社: Packt Publishing (31 May 2019)
Language 语言: English
Book Description
Build efficient forecasting models using traditional time series models and machine learning algorithms.
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
