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How modern time-series models beat spreadsheets for inventory planningContinue reading on Medium »
Time series foundation models bring the LLM playbook to forecasting. Pretrain once on diverse series, then forecast new ones zero-shot.Continue reading on Medium »
Autoregressive rollout and uncertainty propagation. Second in a series on probabilistic forecasting for physical signals. The post Your Model's MSE Is Lying to You: Part II appeare...
A diffusion head for probabilistic time series forecasting: why a forecast can get the mean and the variance right and still describe a future that never happens, and how to fix it...
First in a series on probabilistic forecasting for physical signals. Next: what happens when you roll the forecast forward more than one step. The post Your Model’s MSE Is Lying to...
A fitted statsmodels model computes a more than just the array of numbers most code pulls out of it.
Notes on FFT, Simulation Modeling, and Time-Series ForecastingContinue reading on Medium »
Google Research has released TimesFM-3, a 330 million parameter time series foundation model that forecasts multiple related series in a single forward pass. Unlike every TimesFM c...
Overview Since introducing zero-shot time-series forecasting in BigQuery with AI.FORECAST — initially powered by TimesFM 2.0 and later upgraded to TimesFM 2.5 — you have been able...
A controlled test of Gemini, DeepSeek, ChatGPT, and Claude on leakage, reporting delays, promotion effects, and structural breaks. The post I Hid Four Traps in a Forecasting Task....
Decathlon, one of the world's largest sporting goods retailers, forecasts weekly demand for tens of thousands of products across multiple continents. Learn how they deployed Chrono...
by Joseph Lemaitre, Justin Lessler Forecasting infectious disease incidence can provide important information to guide public health planning, yet it is difficult because epidemic...
Unlock the secrets of time series! Learn how to identify temporal patterns using classical stats and cutting-edge AI Foundation Models. The post Mastering Temporal Patterns: From C...
How a delivery forecast was rebuilt so new ideas can be tested in hours, not weeks, and why a simpler model ensemble beat the original.Continue reading on Picnic Engineering »
Prior to evaluation, there must be someone who defines what good and bad forecasts are. The most significant response to that question is…Continue reading on Medium »
When forecasting high-volatility financial assets like Bitcoin (BTC-USD), standard point-prediction models—whether classical ARIMA or deep neural networks—often fall short. A point...
A single deep learning model trained across 528 stocks, built to forecast volatility.Continue reading on Medium »
by Meryl Theng, Simin Lee, Andrew C. Breed, Sharon Roche, Emily Sellens, Catherine Fraser, Kelly Wood, Chris P. Jewell, Mark A. Stevenson, Chris Baker, Simon M. Firestone Infectio...
Mistral released Timestral, a time series foundation model family from 5M to 209M parameters. The 209M model leads GIFT-Eval, TIME and fev-bench among zero-shot models and runs on...
I was emailed by a journalist writing an article about time series foundation models. Here is my response. While the questions were about a specific new transformer model, the comm...
Time series data are everywhere: heart rhythms on a hospital monitor, sensor streams from a factory floor, stock prices ticking across a trading screen. Teaching a machine to class...
by Lucy M. Devlin, Phi H. Nguyen, Ross N. Cuthbert, Phu Ngoc Doan, Viet Hung Tran, Zichi Zhang, Archie K. Murchie, Connor G. G. Bamford, Jaimie T. A. Dick, Eric R. Morgan, Thai S....
ahead (R) 0.38.1 and its Python wrapper now install in a fraction of the time, by moving almost every heavy modeling dependency from Imports to Suggests and installing them at runt...
A beginner-friendly machine learning project using five years of Indian stock market data.Continue reading on Medium »
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