This repo contains notebooks on time series and forecasting with machine learning
The objective of this competition is to predict 3 months of item-level sales data at different store locations.
5 Feature, 958000 Sample
| Features | Definition |
|---|---|
| date | Date of the sale data. There are no holiday effects or store closures. |
| store | Store ID |
| item | Item ID |
| sales | Number of items sold at a particular store on a particular date. |
lightgbm==3.1.1
matplotlib==3.5.2
numpy==1.22.3
pandas==1.4.4
scikit_learn==1.1.2
seaborn==0.11.2
statsmodels==0.13.2
01_statistical_methods.ipynb - Time Series with Statistical Methods
02_smoothing_methods.ipynb - Time Series with Smoothing Methods
03_airline_passengers.ipynb - Passenger Forecast with Time Series
04_demand_forecasting_lgbm.ipynb - Demand Forecasting with Machine Learning