A Time Series Forecast Model Based on the Most Similar Pattern

The paper presents a time series forecasting model based on the most similar pattern (FMMSP), which identifies historically correlated segments to forecast future values without requiring time series stationarity. The model is validated on electricity consumption and day-ahead price data from Russian wholesale market, achieving mean absolute percentage errors as low as 1.04 % for consumption and 7.0 % for prices.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-24
DOI
https://doi.org/10.5281/zenodo.19724620
Primary Topic
Forecasting Techniques and Applications
Type
article
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article

A Time Series Forecast Model Based on the Most Similar Pattern

Irina Chuchueva
Zenodo (CERN European Organization for Nuclear Research)
Forecasting Techniques and Applications
article

A Time Series Forecast Model Based on the Most Similar Pattern

Irina Chuchueva
article en

Abstract

The paper presents a time series forecasting model based on the most similar pattern (FMMSP), which identifies historically correlated segments to forecast future values without requiring time series stationarity. The model is validated on electricity consumption and day-ahead price data from Russian wholesale market, achieving mean absolute percentage errors as low as 1.04 % for consumption and 7.0 % for prices.

Zenodo (CERN European Organization for Nuclear Research)
Affordable and clean energy
Openalex Percentile: Top 3%
Forecasting Techniques and Applications
DOIOpen Access PDFOpenAlex
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A Time Series Forecast Model Based on the Most Similar Pattern — Irina Chuchueva · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS