I will build time series model for supply chain forecasting
Level 1
Has met certain performance criteria and shows strong potential in the marketplace.
About this Gig
Stop wasting time and money on 2018-era forecasting models. If you are still relying on basic XGBoost, LSTMs, or Prophet for your time series data, you are leaving massive accuracy gains on the table.
I provide next-generation forecasting using the latest Foundation Models like Chronos and TimesFM. Unlike traditional methods that struggle with "drift" and require years of clean historical data, these attention-based models use Zero-Shot learning to understand patterns and trends with human-level intuition.
Whether you are managing retail inventory, energy loads, or financial market trends, I build models that don't just guess numbers but understand context.
What I offer:
- Long-Horizon Forecasting: Stable predictions for weeks or months ahead without the usual error accumulation.
- Multivariate & Covariate Support: Integrating external factors like holidays, pricing shifts, and weather into your forecast.
- Probabilistic Outputs: Instead of one uncertain number, I provide quantile ranges so you can see your best and worst-case scenarios.
- Benchmarking: I'll show you exactly how much more accurate these new models are compared to your current setup.
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FAQ
Why not traditional XGBoost or LSTMs?
Traditional models like XGBoost and LSTMs are "train-from-scratch." They require massive amounts of clean, historical data to learn your specific patterns, and they often struggle with "drift" as soon as market conditions change.
What if I don't have years of historical data? Can you still help?
This is the biggest advantage of using Foundation Models. Older methods usually need at least 2-3 years of history to be reliable. Because the models I use (Chronos, TimesFM) are pre-trained on diverse global datasets, they can provide high-fidelity forecasts with as little as a few weeks of data.

