Time Series Forecasting Training Course

The Time Series Forecasting Training Course equips data analysts, planners, demand and supply chain professionals, finance and operations analysts, and data scientists with the concepts, methods, and practical skills needed to analyze time-ordered data and build reliable forecasts that support planning and decision making. Organizations forecast demand, sales, revenue, costs, inventory, energy consumption, traffic, headcount, and many other quantities that change over time, and the quality of these forecasts directly affects budgeting, planning, service levels, and efficiency. In government entities, banks, utilities, retail and manufacturing, healthcare, and logistics, sound time series forecasting reduces uncertainty, improves resource allocation, and supports better decisions, while poor forecasting leads to stockouts or excess inventory, inaccurate budgets, and missed targets. The course covers time series forecasting from fundamentals to advanced methods and practical application, balancing concepts with hands-on practice. Participants learn the nature of time series data, components such as trend, seasonality, and cycles, and the forecasting process from problem definition to deployment and monitoring. The program then addresses exploratory analysis and visualization, stationarity and transformations, and classical forecasting methods, including moving averages, exponential smoothing and its variants, and decomposition. Participants cover ARIMA and seasonal ARIMA models, including identification, estimation, and diagnostics, and models with external drivers and multiple series. Participants also examine forecast evaluation and accuracy metrics, backtesting and cross-validation for time series, selecting between models and combining forecasts, handling special issues such as missing data, outliers, intermittent demand, and multiple seasonality, and an introduction to machine learning and modern approaches to forecasting, including feature-based and, where appropriate, deep learning methods. Throughout, the course emphasizes practical forecasting workflow, judgmental adjustment, communicating uncertainty, and embedding forecasting into planning processes. The course is delivered through hands-on forecasting exercises using realistic datasets, model building and evaluation labs, and a forecasting project. Participants leave with a time series forecasting workflow guide, a model selection and evaluation checklist, a forecasting accuracy metrics reference, a set of worked forecasting examples, and a forecasting project template. The practical work uses common analytics tools; participants should be comfortable working with data, and prior exposure to basic statistics is helpful.