Social Media Ideas for Time Series Forecasting

3 Statsmodels Tricks for Time Series Analysis & Forecasting

A fitted statsmodels model computes a more than just the array of numbers most code pulls out of it.

Your Model’s MSE Is Lying to You

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...

Your Model's MSE Is Lying to You: Part II

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...

How Time Series Foundation Models Work

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 »

Your Model's MSE Is Lying to You III: Time Series Diffusion

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...

Introducing TimesFM 3.0 with multivariate time-series forecasting in BigQuery

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...

Google AI Releases TimesFM-3: A 330M Parameter Zero-Shot Foundation Model For Multivariate Time Series Forecasting

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...

SliteView — Forecasting Electrical Motor Signals with Bi-LSTM

Notes on FFT, Simulation Modeling, and Time-Series ForecastingContinue reading on Medium »

Mastering Temporal Patterns: From Classical Statistics to Foundation Models

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...

Mean Squared Error (MSE)

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 »

How Decathlon runs demand forecasting at scale with Chronos-2

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...

Hybrid Machine Learning & Financial Econometrics: Building Volatility-Aware Dynamic Forecast Bands for Bitcoin

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...

Unlock powerful insights and forecasts with Predictive AI Functions

Data teams all get asked the same questions: "what will sales be next quarter?,"...

Predicting the Nifty 50’s Next-Day Close with Linear Regression (and What 95% Really Means)

A beginner-friendly machine learning project using five years of Indian stock market data.Continue reading on Medium »

Random Convolutions Get Smarter: New AI Picks Its Own Features for Time Series

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...

I Hid Four Traps in a Forecasting Task. Here Is What Four AI Assistants Did.

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....

Stock Forecasting Based on Deep Learning: Returns up to 180.77% in 1 Year

Package Name: Insider Trades Recommended Positions: Long Forecast Length: 1 Year (9/14/25 - 9/14/26) I Know First Average: 29.04% Read The Full Forecast...

Mistral AI Releases Timestral: A 209M Forecasting Model Family That Outperforms Google’s TimesFM-3

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...

Building a Modular Demand Forecast: How We Made Testing New Ideas a Breeze

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 »

Generative diffusion models for spatiotemporal influenza forecasting

by Joseph Lemaitre, Justin Lessler Forecasting infectious disease incidence can provide important information to guide public health planning, yet it is difficult because epidemic...

Deep Learning Transforms Spatiotemporal Prediction: Reviewing Methods and Applications

A new review argues that deep learning is reshaping how scientists forecast systems that change across both space and time, from moving traffic and polluted air to evolving weather...

[iVISTscalp5]: DAX. Forecast First. Market Reaction Afterwards//October 12-16, 2026

How Modern Supply Chains Win on Forecasting Precision

The organizations that succeed are the ones that treat forecasting as a strategic capability rather than a back-office function

Predicto Atlas: One Model to Forecast Them All

A single deep learning model trained across 528 stocks, built to forecast volatility.Continue reading on Medium »