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Time Series Analysis in R: Forecasting, State-Space Modeling, and Applied Econometric Methods by Julian K. Mercer
English | February 21, 2026 | ASIN: B0GPML4G47 | 502 pages | EPUB | 0.51 Mb
Reactive Publishing Accurate forecasting and rigorous time series modeling are central to modern data analysis in finance, economics, and applied research. Time Series Analysis in R provides a structured, implementation-focused guide to modeling temporal data using R's most widely adopted statistical and econometric libraries.
Beginning with core concepts such as stationarity, autocorrelation, and ARIMA processes, the book progresses into advanced forecasting techniques, state-space modeling, and likelihood-based estimation. Emphasis is placed on practical implementation, diagnostic testing, and model validation rather than abstract theory alone.
Readers will learn how to:
Build and evaluate AR, MA, ARIMA, and SARIMA models
Implement exponential smoothing and dynamic regression
Construct and estimate state-space models
Apply Kalman filtering and smoothing methods
Perform model diagnostics and residual analysis
Compare out-of-sample forecast performance
Integrate econometric frameworks for applied research contexts
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