AI-Powered Time Series Forecasting (Sales, Demand, Capacity)

Gather 2+ years of data: sales by product/region, customer demand, website traffic, support ticket volume, infrastructure capacity. Include external factors: seasonality, promotions, holidays, market events. Clean data: handle missing values, outliers, anomalies. Ensure timestamps are consistently timezone-adjusted; mixing UTC and local times is a common error that creates phantom seasonality patterns. Document every known data anomaly (system outage, one-time promotion, COVID lockdown) so you can handle these periods during model training rather than letting them corrupt seasonal patterns.

Test multiple approaches: ARIMA, Prophet (Facebook), LSTM (deep learning), AWS Forecast, Google Vertex AI Forecasting. Evaluate on: accuracy (MAPE, RMSE), ability to handle seasonality, incorporation of external regressors. Choose best performer. Prophet works well for business data with strong seasonality and holiday effects and requires minimal tuning, making it a good baseline. LSTM models can capture complex non-linear patterns but require 10x more data and tuning effort; only invest in deep learning if Prophet's accuracy plateau is insufficient for your use case.

Split data: train on 80%, test on 20%. Train models separately for: each product line, each region, overall company. Incorporate external factors: marketing spend, macroeconomic indicators, competitor activity. Validate accuracy on holdout set. Use expanding-window cross-validation (not random splits) to mimic real-world forecasting conditions where you always predict the future from the past. Report forecast accuracy at multiple horizons (1-week, 4-week, 12-week) since accuracy degrades with horizon length and stakeholders need to understand this trade-off.

Schedule weekly forecast updates: ingest latest data, retrain models, generate predictions for next 12 weeks. Output: point estimates, 80% and 95% confidence intervals. Publish to dashboards. Alert teams on significant forecast changes. Track actual vs. predicted. Publish both the point forecast and the 80% confidence interval; decision-makers need to know the uncertainty range to set appropriate safety margins. Alert the data team when actual values fall outside the 95% confidence interval for three consecutive periods, indicating potential model breakdown.

Monitor forecast accuracy over time. When actual diverges from prediction, investigate: did market conditions change? Was there a data quality issue? Retrain models with new data. Adjust for systematic bias. Share insights with business teams. Maintain a forecast accuracy leaderboard comparing AI models to the previous manual forecasting method. When the AI model underperforms manual forecasts for a specific product or region, investigate rather than assume the model is always right. Often the issue is a missing external regressor that a domain expert would naturally account for.