{"product_id":"time-series-forecasting-for-analysts-corin-halstead-9798176036015","title":"Time Series Forecasting for Analysts: Model Trends, Seasonality, Change, and Uncertainty","description":"\u003cp\u003e\u003cb\u003eForecasting is easy to start. Forecasting well is much harder.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eA 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.\u003c\/p\u003e\u003cp\u003eBut a forecast can still fail.\u003c\/p\u003e\u003cp\u003eThe 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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eTime Series Forecasting for Analysts\u003c\/b\u003e teaches the practical reasoning required to build forecasts that are accurate, explainable, and useful for real decisions.\u003c\/p\u003e\u003cp\u003eRather than treating forecasting as a race to find the most advanced algorithm, this book presents forecasting as a disciplined analytical workflow: \u003c\/p\u003e\u003cp\u003e\u003cb\u003eUnderstand the signal → choose appropriate assumptions → test against reality → communicate uncertainty → improve with new evidence.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eInside, you will learn how to: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eDefine forecasting problems using the target, frequency, horizon, information availability, and business decision\u003c\/li\u003e\n\u003cli\u003eUnderstand why time-series data requires different validation than ordinary machine learning\u003c\/li\u003e\n\u003cli\u003eIdentify level, trend, seasonality, autocorrelation, variance changes, missingness, and structural breaks\u003c\/li\u003e\n\u003cli\u003eBuild strong naive and seasonal-naive baselines before trusting complex models\u003c\/li\u003e\n\u003cli\u003eModel trends using regression, local trends, damped trends, changepoints, and constrained growth\u003c\/li\u003e\n\u003cli\u003eHandle recurring patterns, holidays, promotions, calendar effects, and multiple seasonalities\u003c\/li\u003e\n\u003cli\u003eDistinguish temporary outliers from permanent regime changes\u003c\/li\u003e\n\u003cli\u003eApply exponential smoothing and ETS models for evolving level, trend, and seasonality\u003c\/li\u003e\n\u003cli\u003eUnderstand ARIMA, SARIMA, and dynamic regression with external drivers\u003c\/li\u003e\n\u003cli\u003eCreate machine-learning forecasts using lag features, rolling statistics, calendar features, and tree-based models\u003c\/li\u003e\n\u003cli\u003eAvoid leakage when engineering forecasting features\u003c\/li\u003e\n\u003cli\u003eBuild probabilistic forecasts using prediction intervals, quantiles, simulation, and conformal prediction\u003c\/li\u003e\n\u003cli\u003eEvaluate forecasts with rolling-origin backtests instead of unrealistic random splits\u003c\/li\u003e\n\u003cli\u003eSelect metrics that match business consequences rather than chasing one universal score\u003c\/li\u003e\n\u003cli\u003eCompare models using accuracy, stability, bias, uncertainty, interpretability, and operational value\u003c\/li\u003e\n\u003cli\u003eMonitor forecast performance after deployment through drift, revisions, bias, and changing conditions\u003c\/li\u003e\n\u003cli\u003eBuild professional forecasting workflows with reproducible pipelines, versioning, governance, and ownership\u003c\/li\u003e\n\u003cli\u003eUnderstand where modern forecasting foundation models fit into the analyst's toolkit\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eThe book emphasizes a key forecasting principle: \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA forecast is not just a prediction. It is a structured claim about the future built from available information, assumptions, and uncertainty.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eIt 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.\u003c\/p\u003e\u003cp\u003eWhether you are a \u003cb\u003edata analyst, business analyst, product analyst, operations analyst, data scientist, forecasting specialist, supply-chain professional, finance analyst, or analytics engineer\u003c\/b\u003e, this book will help you build forecasts that decision-makers can understand and trust.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eDo not just predict the future. Build forecasts that help people make better decisions.\u003c\/b\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Corin Halstead\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798176036015\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 09\/21\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 170\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.47lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.00h x 6.00w x 0.43d","brand":"Corin Halstead","offers":[{"title":"Paperback","offer_id":50780097216767,"sku":"9798176036015","price":18.99,"currency_code":"USD","in_stock":true}],"url":"https:\/\/www.whiterainbookhouse.com\/products\/time-series-forecasting-for-analysts-corin-halstead-9798176036015","provider":"WR Book House","version":"1.0","type":"link"}