# 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](../../26-time-series-analysis/README.md) if you need stronger conceptual depth first.

## What Comes Next

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