Forecasting missing data in time series with the application of hybrid models – theoretical and empirical approach

Abstract

The main goal of the article is to present the possibilities of forecasting missing observations in time series for hourly data with the application of hybrid models. Hybrid time series models and regression models with complex seasonal fluctuations were used in the study. Complex fluctuations for hourly data can be either a sum or a product of fluctuations of annual, weekly and daily cycles, while fluctuations the length of a cycle expressed by an even number (12-month and 24-hour ones) can be described using regular hierarchical models. The theoretical considerations were illustrated by an empirical analysis of the demand for electricity in hourly periods in a selected agglomeration. The statistical data covered three consecutive years of the first decade of the 2000s. The data were provided by an electricity distribution company and included in the Data Bank of the Department of Applied Mathematics in Economics of the West Pomeranian University of Technology in Szczecin. It was assumed that non-systematic gaps occur with regard to all types of seasonal fluctuations. The obtained results indicate the usefulness of hybrid models in forecasting economic phenomena subject to very frequent observations.