This project builds an end-to-end time-series forecasting and seasonality validation pipeline for movie rankings.
While SARIMA models can detect seasonality, they often over-flag seasonal behavior. This work introduces a post-forecast analytical layer to distinguish true seasonal movies from noisy or non-seasonal ones using forecast behavior patterns.
Traditional time-series models frequently label movies as seasonal even when no meaningful seasonal demand exists.
The objective of this project is to:
- Forecast weekly movie rankings
- Identify true seasonality
- Reduce false seasonal classifications
- Produce interpretable seasonal insights
.
├── Time-series_forecast.ipynb # Initial Model Selection
├── time_series_loop_model_SARIMA.ipynb # Model training & selection
├── some_checking.ipynb # Seasonality post-processing
├── seasonal_movie_spikes_analysis.csv # Final analysis output
└── README.md