Time Series Forecasting for Analysts: Model Trends, Seasonality, Change, and Uncertainty (Paperback) (ISBN-13: 9798176036015)

Forecasting is easy to start. Forecasting well is much harder.

A spreadsheet can produce a forecast in minutes. A machine-learning library can generate predictions with a few lines of code. A pretrained model can produce a future estimate almost instantly.

But a forecast can still fail.

The target may be poorly defined. The evaluation may accidentally use future information. A seasonal pattern may have changed. A trend may be temporary. A prediction interval may hide important uncertainty. A complex model may lose to a simple baseline when tested honestly.

Time Series Forecasting for Analysts teaches the practical reasoning required to build forecasts that are accurate, explainable, and useful for real decisions.

Rather than treating forecasting as a race to find the most advanced algorithm, this book presents forecasting as a disciplined analytical workflow:

Understand the signal → choose appropriate assumptions → test against reality → communicate uncertainty → improve with new evidence.

Inside, you will learn how to:

• Define forecasting problems using the target, frequency, horizon, information availability, and business decision
• Understand why time-series data requires different validation than ordinary machine learning
• Identify level, trend, seasonality, autocorrelation, variance changes, missingness, and structural breaks
• Build strong naive and seasonal-naive baselines before trusting complex models
• Model trends using regression, local trends, damped trends, changepoints, and constrained growth
• Handle recurring patterns, holidays, promotions, calendar effects, and multiple seasonalities
• Distinguish temporary outliers from permanent regime changes
• Apply exponential smoothing and ETS models for evolving level, trend, and seasonality
• Understand ARIMA, SARIMA, and dynamic regression with external drivers
• Create machine-learning forecasts using lag features, rolling statistics, calendar features, and tree-based models
• Avoid leakage when engineering forecasting features
• Build probabilistic forecasts using prediction intervals, quantiles, simulation, and conformal prediction
• Evaluate forecasts with rolling-origin backtests instead of unrealistic random splits
• Select metrics that match business consequences rather than chasing one universal score
• Compare models using accuracy, stability, bias, uncertainty, interpretability, and operational value
• Monitor forecast performance after deployment through drift, revisions, bias, and changing conditions
• Build professional forecasting workflows with reproducible pipelines, versioning, governance, and ownership
• Understand where modern forecasting foundation models fit into the analyst's toolkit

The book emphasizes a key forecasting principle:

A forecast is not just a prediction. It is a structured claim about the future built from available information, assumptions, and uncertainty.

It also teaches analysts to treat forecasting as a decision system. The best forecast is not always the one with the lowest statistical error; it is the one that improves the decision while considering timing, stability, explainability, and the cost of being wrong.

Whether you are a data analyst, business analyst, product analyst, operations analyst, data scientist, forecasting specialist, supply-chain professional, finance analyst, or analytics engineer, this book will help you build forecasts that decision-makers can understand and trust.

Do not just predict the future. Build forecasts that help people make better decisions.