---
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](https://huggingface.co/raws-labs/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

1. Upload a CSV file with a value column (or paste numeric values directly)
2. Select the frequency/domain of your time series
3. Choose a forecast horizon (1–512 steps)
4. 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](https://huggingface.co/raws-labs/tinycast)
- **Code**: [github.com/raws-labs/tinycast](https://github.com/raws-labs/tinycast)
- **Paper**: [arXiv:2608.15767](https://arxiv.org/abs/2608.15767)
- **License**: Apache-2.0