Time Series Forecasting in Practice
Classical models, gradient boosting and deep learning for demand and operations
By Houssam Kodad
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About this book
What's inside
Forecasting is where statistics meets the messiness of real operations — promotions, holidays, stockouts and structural breaks. This book takes a pragmatic path from classical methods through gradient boosting to modern deep models, always asking which approach earns its complexity. You'll learn to validate forecasts honestly, quantify uncertainty, and build pipelines that forecast thousands of series without falling apart.
What you'll learn
Skills you'll walk away with
- Decompose series into trend, seasonality and residuals
- Apply ARIMA and exponential smoothing where they shine
- Frame forecasting as supervised learning for boosting
- Build calendar, lag and holiday features that help
- Validate with backtesting and rolling-origin evaluation
- Produce prediction intervals, not just point forecasts
- Forecast many series at scale with hierarchical methods
Table of contents
9 chapters-
01
Forecasting as a Business Problem
- · What a forecast is for
- · Accuracy vs decisions
- · Choosing an error metric
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02
Decomposition and Stationarity
- · Trend and seasonality
- · Differencing and transforms
- · Tests for stationarity
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03
Classical Models That Still Win
- · Exponential smoothing
- · ARIMA and SARIMA
- · When simple beats complex
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04
Forecasting as Supervised Learning
- · Reframing the problem
- · Feature windows and horizons
- · Direct vs recursive strategies
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05
Gradient Boosting for Forecasts
- · Lag and rolling features
- · Holidays and external regressors
- · Multi-series single models
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06
Deep Learning for Sequences
- · When neural nets help
- · Global models across series
- · Practical training tips
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07
Validating Forecasts Honestly
- · Backtesting and rolling origin
- · Avoiding leakage in CV
- · Comparing against naive baselines
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08
Uncertainty and Intervals
- · Quantile forecasts
- · Conformal prediction
- · Communicating uncertainty
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09
Forecasting at Scale
- · Hierarchical reconciliation
- · Thousands of series in a pipeline
- · Monitoring forecast quality
This is the full chapter list — exactly what you'll receive in the PDF.
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