sktime/sktime

[ENH] Verify TinyTimeMixerForecaster exogenous and few-shot capability

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#8,522 opened on Jul 11, 2025

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 (7 comments) (0 reactions) (0 assignees)Python (7,162 stars) (1,192 forks)batch import
enhancementgood first issuemodule:forecasting

Description

The original paper describes exogenous support and few-shot capability, but the current sktime implementation seems not to support it. Paper: 📄 TinyTimeMixer Paper Reference: Tiny Time Mixers(TTMs): Powerful Zero/Few-Shot Forecasting Models by IBM Code: 🛠️ sktime ttm.py implementation

Contributor guide

Tech stack
python
Domain
machine learning
Issue type
feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
fresh
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
mostly clear
Prerequisites
Basic knowledge of time series forecastingFamiliarity with sktime forecasting moduleReading the TinyTimeMixer paper
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
60
Research direction
Start by reviewing the TinyTimeMixer paper's sections on exogenous and few shot learning. Then examine the current implementation in sktime/forecasting/ttm.py, particularly the TTM class and its fit/predict methods. Check the open issues and discussions in the sktime repository for related work. The issue's comments may contain additional guidance from maintainers. The goal is to implement support for exogenous features and few shot scenarios following the paper's architecture.
[ENH] Verify TinyTimeMixerForecaster exogenous and few-shot capability · sktime/sktime#8522 | Good First Issue