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Forecasting Pipeline

Build time series forecasting pipeline including data preparation, model selection, training, validation, and automated forecast generation.

Complexity: Complex | Duration: 30m+ | Category: Devops

Tags: analytics forecasting time-series prediction

Workflow Diagram​

Steps​

Step 1: Data preparation​

Agent: data

engineer - Extract historical time series data

Step 2: Stationarity check​

Agent: data

scientist - Check for stationarity, apply differencing if needed

Step 3: Seasonality detection​

Agent: data

scientist - Detect seasonal patterns, trends

Step 4: Model selection​

Agent: data

scientist - Choose ARIMA, Prophet, LSTM, or XGBoost

Step 5: Train/test split​

Agent: data

scientist - Time-based split (train on historical, test on recent)

Step 6: Model training​

Agent: data

scientist - Train forecasting model

Step 7: Validation​

Agent: data

scientist - Validate on test set, calculate MAPE, RMSE

Step 8: Forecast generation​

Agent: data

scientist - Generate forecasts for next N periods

Usage​

To execute this workflow:

/workflow devops/forecasting-pipeline.workflow

See other workflows in this category for related automation patterns.