Time Series Forecasting

Fundamentals, ARIMA/SARIMA, Prophet, LSTM, anomaly detection, and forecasting competitions.

Use this subtrack when you want forecasting-specific project work rather than general tabular ML. It fits best if you want business-facing planning, demand, finance, or monitoring use cases.

How To Use This Subtrack Well

• Start with classical baselines before trying sequence models.
• Evaluate forecasts with realistic backtesting, not a single train/test split.
• Pair this subtrack with ../../26-time-series-analysis/README.md if you need stronger conceptual depth first.

What Comes Next

• Continue to ../README.md for the broader practical data science roadmap.
• Continue to ../../26-time-series-analysis/README.md for deeper forecasting foundations.
• Continue to ../../09-mlops/README.md if you want deployment and monitoring for forecast systems.