---
title: "Evaluating Time Series Foundation Models for Electricity Price Forecasting: Contamination Risk, Distributional Shifts, and Covariate Dependence — Stuff That Spins"
description: "arXiv:2607.02623v1 Announce Type: new Abstract: Time series foundation models (TSFMs) have shown strong zero-shot forecasting performance, but their generaliza…"
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date: "2026-07-07T04:00:00+00:00"
modified: "2026-07-07T06:05:00.54961+00:00"
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# Evaluating Time Series Foundation Models for Electricity Price Forecasting: Contamination Risk, Distributional Shifts, and Covariate Dependence

**Source:** Unknown  
**Published:** July 7, 2026  
**Original:** https://arxiv.org/abs/2607.02623  

## On this page

- [Overview](#overview)

<a id="overview"></a>

## Overview

arXiv:2607.02623v1 Announce Type: new Abstract: Time series foundation models (TSFMs) have shown strong zero-shot forecasting performance, but their generalization in covariate-driven, non-stationary settings is underexplored. Electricity price forecasting (EPF) presents a challenging testbed due to complex temporal dependencies, distributional shifts, and strong reliance on structural and contextual information. We propose a two-dataset-benchmarking framework for EPF to mitigate contamination r

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