Static Order Up To Level Simulation Starter NB With Polars/Numba. This can easily be made forecast driven!

The "official" code

An 13-week seasonal moving average combined with a forecast-driven policy with a 4 weeks coverage

A forward-looking graphic view of the inventory position and balance

Learn how to use MLForecast to leverage machine learning models for time series forecasting

Use deep learning for time series forecasting

Implementation of Deep RL for the VN2 Inventory Planning challenge

Format and preprocess your data for time series forecasting

Describes how to interpret the Inventory Snapshot

Use statistical models for time series forecasting and learn hierarchical reconciliation methods.

This notebook demonstrates the use of StatsForecast and NeuralForecast methods to analyze time series data. It includes the methodology to identify the demand type of each time series, helping to understand patterns such as intermittent, smooth, or seasonal demand.

Can AI agents autonomously create state-of-the-art forecasting models? We're about to find out!

Weighted ensemble from AutoMFLES and Lightgbm by minimising a desired error using scipy optimise to reduce regression to the mean.