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Hands-On Time Series Analysis with R - Perform time series analysis and forecasting using R (Paperback)
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Hands-On Time Series Analysis with R - Perform time series analysis and forecasting using R (Paperback)
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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 Master statistics and
implement time-series methods using examples mentioned 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 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 Who this book is forHands-On Time Series
Analysis with R is ideal for data analysts, data scientists, and
all R developers who are looking to perform time series analysis to
predict outcomes effectively. A basic knowledge of statistics is
required; some knowledge in R is expected, but not mandatory.
General
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