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 ...
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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.
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