metadata
title: TinyCast Forecaster
emoji: 📈
colorFrom: gray
colorTo: blue
sdk: gradio
sdk_version: 6.25.0
app_file: app.py
short_description: TinyCast zero-shot probabilistic time-series forecasting
python_version: '3.12'
startup_duration_timeout: 30m
TinyCast Forecaster
A Gradio demo for TinyCast, an attention-free, 146,505-parameter time-series foundation model that forecasts unseen series zero-shot and returns nine quantile forecasts.
How it works
- Upload a CSV file with a value column (or paste numeric values directly)
- Select the frequency/domain of your time series
- Choose a forecast horizon (1–512 steps)
- Get a probabilistic forecast plot with quantile confidence bands
The model replaces self-attention with dilated causal convolutions and a zero-parameter normalized-periodogram phase prior, so periodicity is computed from the context instead of learned. Every learned operation is a convolution, a matrix multiplication or a normalization, so the model streams in constant memory and runs on CPU.
Model
- Weights: raws-labs/tinycast
- Code: github.com/raws-labs/tinycast
- Paper: arXiv:2608.15767
- License: Apache-2.0