Time Series Analysis with R: A Practical Guide to Modeling and Forecasting

Time series analysis in R blends statistics and modern ML to explore, model, and predict data recorded over time. It’s essential across finance, operations, marketing, health, and more—helping you uncover patterns, anticipate what comes next, and make better decisions.

What this book covers

• What & Why
  Time series are observations captured at consistent time points and often display trend, seasonality, and autocorrelation. The goal is to detect and model these dynamics to generate reliable insights and forecasts.

• Data preparation
  Build clean, evenly spaced series; impute gaps, tame outliers, and reduce noise so models learn real structure rather than artifacts.

• Exploratory analysis
  Visualize levels and changes over time, diagnose seasonality/trends, compute summaries, and test for stationarity before modeling.

• Decomposition
  Disentangle the series into trend, seasonal, and remainder components (e.g., with STL) to understand each driver separately.

• Modeling & forecasting
  Fit models that match the data’s behavior: ARIMA and exponential smoothing families, plus ML approaches such as LSTM for complex, non-linear patterns.

• Validation
  Use train/test splits and rolling-origin evaluation; compare models with MAE, RMSE, and other accuracy metrics against held-out observations.

• Anomaly detection
  Flag unexpected departures from normal patterns to surface rare events, faults, or sudden shifts.

• Feature engineering
  Enhance predictability with lags, rolling windows, calendar effects, and other derived features tailored to your domain.

• Interpreting forecasts
  Read point forecasts and intervals in context, translate trends into actions, and communicate uncertainty for informed decision-making.

This book gives you a practical, end-to-end workflow in R—from raw time-stamped data to validated forecasts you can trust.