metis-1-preview

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

ChakraTS

Y-Hat Labs

ChakraTS is a zero-shot probabilistic time-series forecasting model from YHat Labs, served as a hosted API. This repository holds the model card; there are no weights to download. Send a batch of series and a horizon, get back nine quantiles (10% to 90%) for every future step. ChakraTS forecasts zero-shot: nothing is trained or fitted on your data, and fine-tuning on customer data is not part of this release. It accepts known-future covariates, works at any regular frequency from minutes to years, and returns the per-step prediction intervals that scheduling, procurement, bidding and inventory decisions depend on. Access is by API key, issued on request. One call forecasts a batch of…

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…