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.