Krispin Hands-On Time Series Analysis with R

Perform time series analysis and forecasting using R
1. Auflage 2024
ISBN: 978-1-78862-404-6
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 448 Seiten

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
- Learn statistics and implement time-series methods with the help of examples
Book DescriptionTime-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.What you will learn - Visualize time-series data and derive useful insights
- Study auto-correlation and understand 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
- Discover time-series models such as ARIMA, Holt-Winters, and more
- Evaluate high-performance forecasting solutions
Who this book is forHands-On Time Series Analysis with R is ideal for data analysts, data scientists, and R developers looking to perform time-series analysis to predict outcomes effectively. Basic knowledge of statistics is required to understand the concepts covered in this book. Also, some experience in R will be helpful.

Autoren/Hrsg.

Krispin, Rami

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsvisualisierung

Inhaltsverzeichnis

Table of Contents - 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

Über Autor(innen)

Krispin Rami :

Rami Krispin is a data scientist at a major Silicon Valley company, where he focuses on time series analysis and forecasting. In his free time, he also develops open source tools and is the author of several R packages, including the TSstudio package for time series analysis and forecasting applications. Rami holds an MA in Applied Economics and an MS in actuarial mathematics from the University of MichiganAnn Arbor.