Template for forecasting data science project and identify consumption profiles in time series
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Updated
Oct 3, 2023 - Jupyter Notebook
#
intermittent-demand
Here are 4 public repositories matching this topic...
A production-ready, end-to-end system for spare parts inventory management. This project combines advanced forecasting (TSB, Prophet, SARIMAX), mathematical optimization (CPLEX), and supply chain simulation (anyLogistix) to achieve 98.9% forecast accuracy and a 39% ROI improvement.
python data-science simulation cplex operations-research prophet demand-forecasting sarimax portfolio-project network-optimization supply-chain-management tsb inventory-optimization intermittent-demand m5-forecasting anylogistix greenfield-analysis
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Jul 12, 2025 - Jupyter Notebook
Benchmarking time-series foundation models (Chronos-Bolt, zero-shot) vs. supervised (PatchTST) and classical (seasonal-naive, Croston) baselines on the M5 Walmart dataset, scored with MASE and WQL. No single model dominates: foundation/deep models win on dense SKUs, classical methods win on the intermittent tail.
benchmark time-series pytorch forecasting retail chronos demand-forecasting quantile-regression m5 intermittent-demand foundation-models patchtst
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Jun 14, 2026 - Python
Demand-regime instability detection for daily SKU demand series with STABLE / DRIFT / SHIFT state outputs.
python time-series inventory jupyter-notebook forecasting change-detection research-prototype intermittent-demand regime-detection
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Updated
May 15, 2026 - Python
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