AI TRAINING
Time-Series Forecasting for Operations Teams
Build reliable forecasting pipelines that move beyond spreadsheets into production-ready ML models.
What it covers
This practitioner-level programme teaches operations, supply chain, and finance analysts how to design and deploy time-series forecasting systems. Participants progress from classical statistical methods (ARIMA, Exponential Smoothing) through modern ML approaches (Prophet, LightGBM, TimeGPT, Nixtla) and learn to evaluate models rigorously using backtesting frameworks. The course includes hands-on labs building end-to-end forecasting pipelines and integrating outputs into operational decision workflows.
What you'll be able to do
- Select the appropriate forecasting method (statistical vs. ML vs. foundation model) for a given operational problem and dataset
- Implement a backtesting harness to evaluate and compare forecast accuracy using MAPE, RMSE, and coverage metrics
- Build a Prophet or Nixtla StatsForecast pipeline with custom seasonalities and external regressors from raw operational data
- Deploy a forecasting model as a scheduled pipeline with automated retraining triggers and drift monitoring
- Communicate forecast uncertainty to non-technical stakeholders using confidence intervals and scenario ranges
Topics covered
- Classical forecasting: ARIMA, Exponential Smoothing, Holt-Winters
- ML-based forecasting: LightGBM, XGBoost with lag features
- Foundation models: Prophet, TimeGPT, Nixtla StatsForecast
- Backtesting and cross-validation for time-series evaluation
- Feature engineering: seasonality, holidays, external regressors
- Forecast uncertainty and confidence intervals
- Production pipeline design: scheduling, monitoring, retraining
- Integrating forecasts into operational dashboards and planning tools
Delivery
Delivered as a blended programme over 3–5 weeks: two live instructor-led sessions per week (90 minutes each) combined with async lab work between sessions. Labs use Jupyter notebooks with real-world datasets (retail demand, energy consumption, financial revenue). Participants bring one internal dataset to apply learnings directly. Hands-on ratio is approximately 60% labs, 40% instruction. Remote delivery via video conferencing; in-person cohort delivery available on request for groups of 10+.
What makes it work
- Anchoring every model choice to a business metric (e.g., inventory holding cost, stockout rate) rather than pure statistical accuracy
- Establishing a baseline naive forecast at the outset so improvement is always measurable and communicable
- Involving end-users (planners, buyers, finance) in reviewing forecast outputs during the programme, not just at the end
- Automating retraining and monitoring from day one so the pipeline remains operational without manual intervention
Common mistakes
- Jumping straight to complex ML models without establishing a statistical baseline, making it impossible to measure real improvement
- Using random train/test splits instead of time-ordered backtesting, leading to optimistic and invalid accuracy scores
- Ignoring forecast uncertainty and presenting point estimates to planners who then make binary go/no-go decisions on them
- Building a one-shot forecast model with no retraining schedule, causing silent degradation as patterns shift
When NOT to take this
This training is not the right fit for teams that do not yet have clean, consistent historical data at the required granularity — if your organisation cannot export 18+ months of reliable transaction or operational records, the value of advanced forecasting methods will be severely limited and a data quality initiative should come first.
Providers to consider
Sources
This training is part of a Data & AI catalog built for leaders serious about execution. Take the free diagnostic to see which trainings your team needs.