ChakraTS

Model · Time series forecasting

TiRex

NX-AI

This repository provides the pre-trained forecasting model TiRex introduced in the paper TiRex is a 35M parameter pre-trained time series forecasting model based on xLSTM. TiRex is a pre-trained model that performs time series forecasting without requiring any training on your data. Simply download and use it. TiRex provides both point estimates and quantile estimates. - State-of-the-art Performance over Long and Short Horizons: TiRex achieves top scores in various time series forecasting benchmarks, see GiftEval and ChronosZS. These benchmark show that TiRex provides great performance for both long and short-term forecasting. A detailed documentation of TiRex can be found here…

Model · Time series forecasting

TiRex-1.1-gifteval

NX-AI

The 1.1 release introduces long period normalisation, a method applied solely during inference. This specific version (1.1-gifteval) includes the 1.1 improvements plus the pretraining dataset has been cleaned to remove overlaps with the GIFT-Eval test dataset. TiRex is a time-series foundation model designed for time series forecasting, with the emphasis to provide state-of-the-art forecasts for both short- and long-term forecasting horizon. TiRex is 35M parameter small and is based on the xLSTM architecture allowing fast and performant forecasts. The model is described in the paper TiRex: Zero-Shot Forecasting across Long and Short Horizons with Enhanced In-Context Learning. TiRex performs…

Model · Time series forecasting

metis-1-preview

Parisi Labs, Inc.

Metis is Parisi Labs' family of world models for physical systems, starting with energy. Metis 1 Preview is its first generation: probabilistic electricity price forecasts that read prices, load, generation and weather and forecast the full range of what power could cost at a location. This card describes the model. Weights are not distributed; forecasts are served through Ask the Grid, the Forecast API and MCP. Price is the target. Recent real-time prices, published day-ahead prices, load, generation and weather explain what the grid is doing, and every input is lined up to when it was actually available. Responses carry the issue time, quantile bands per interval, the settled price once…

Engineered & Architected by Suryaansh Prithvijit Singh (@Ultron28 / @Ultron09) The ULTRON Financial Conformer (1.189B) is a large-scale foundation model specifically designed to ingest raw minute-level OHLCV candlestick time series across liquid US equities and generate multi-horizon continuous returns and directional probability forecasts. Unlike conventional time-series transformers (e.g. PatchTST, Chronos, TimesFM) that treat financial candlesticks as 1D scalar sequences, this architecture treats the microstructure as a continuous multi-scale physical manifold. It combines: 1. 6-Scale Temporal Inception Tokenizer: Continuous 1D convolutions spanning $[3\text{m}, 7\text{m}, 15\text{m}…

Model · Time series forecasting

granite-timeseries-patchtst

IBM Granite

PatchTST is a transformer-based model for time series modeling tasks, including forecasting, regression, and classification. This repository contains a pre-trained PatchTST model encompassing all seven channels of the ETTh1 dataset. This particular pre-trained model produces a Mean Squared Error (MSE) of 0.3881 on the test split of the ETTh1 dataset when forecasting 96 hours into the future with a historical data window of 512 hours. For training and evaluating a PatchTST model, you can refer to this demo notebook. The PatchTST model was proposed in A Time Series is Worth 64 Words: Long-term Forecasting with Transformers by Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam. At…

Model · Time series forecasting

granite-timeseries-ttm-r2

IBM Granite

TinyTimeMixers (TTMs) are compact pre-trained models for Multivariate Time-Series Forecasting, open-sourced by IBM Research. With model sizes starting from 1M params, TTM introduces the notion of the first-ever “tiny” pre-trained models for Time-Series Forecasting. The paper describing TTM was accepted at NeurIPS 24. TTM outperforms other models demanding billions of parameters in several popular zero-shot and few-shot forecasting benchmarks. TTMs are lightweight forecasters, pre-trained on publicly available time series data with various augmentations. TTM provides state-of-the-art zero-shot forecasts and can easily be fine-tuned for multi-variate forecasts with just 5% of the training…