Time Series Forecasting Models

TimeBraid: Unifying Time Series and Language for Understanding and Forecasting TimeBraid combines numerical time series and natural language in one model. It can explain an observed series in words and predict future values using both its history and relevant text. - Understand time series. Describe trends, answer questions about temporal patterns, and compare multiple series in natural language. - Forecast with context. Combine observed values with text about events or conditions to produce numerical forecasts. - Use one interface. Supply numeric arrays and messages through the Hugging Face processor; choose text output or a forecast for one selected series. The checkpoint includes both…

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…

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}…