Modele Hybrydowe W Prognozowaniu Brakujących Danych W Szeregach O Bardzo Wysokiej Częstotliwości Obserwowania

Zawadzki, Jan

doi:10.21005/oe2018.92.3.07

Cited by 2 publications

(2 citation statements)

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“…The research conducted by Szmuksta-Zawadzka and Zawadzki (2014Zawadzki ( , 2016 and Zawadzki (2018) revealed that forecasting models should be selected according to predictors with minimal estimates of relative errors of interpolation forecasts (MAPEI) or extrapolation forecasts (MAPE E ). Accordingly, for each of these criteria, the authors listed above calculated type II error of forecasts as auxiliary values: MAPE I_E and MAPE E_I , respectively.…”

Section: Przedmiot I Zakres Badań Empirycznychmentioning

confidence: 99%

“…Szmuksta-Zawadzka and Zawadzki (2011Zawadzki ( , 2014Zawadzki ( , 2015Zawadzki ( , 2016, and then Zawadzki (2018Zawadzki ( , 2020) also conducted long-standing research on the methods used for forecasting missing data in hourly time series with triple-complex seasonal fluctuations, for both systematic and non-systematic gaps. This research also explores the missing variants, and summarizes the obtained findings.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Methods for Hourly Electricity Demand Forecasting in the Absence of Data – A Case Study

Zawadzki

2023

Economic and Regional Studies / Studia Ekonomiczne I Regionalne

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Scope and purpose of work This paper examines the impact of the number of gaps in data, the analytical form, and the model type selection criterion on the accuracy of interpolation and extrapolation forecasts for hourly data. Materials and methods Forecasts were developed on the basis of predictors that are based on: classical time series forecasting models and regression time series forecasting models, hybrid time series forecasting models and hybrid regression forecasting models for uncleared series, and exponential smoothing models for cleared series of two or three types of seasonal fluctuations, with minimum estimates of errors in interpolation or extrapolation forecasts. Results Adaptive and hybrid regression models have proved to have the most favorable predictive properties. Most hybrid time series models for systematic and non-systematic gaps and for both analytical forms are single models that generally describe fluctuations within a 24-hour cycle. Conclusions The lowest estimators of prediction errors involving interpolation were obtained for exponential smoothing models, followed by hybrid regression models. A reverse sequence was obtained for extrapolative forecasting.

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Section: Przedmiot I Zakres Badań Empirycznychmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Methods for Hourly Electricity Demand Forecasting in the Absence of Data – A Case Study

Zawadzki

2023

Economic and Regional Studies / Studia Ekonomiczne I Regionalne

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

Zawadzki

2020

Wiadomości Statystyczne. The Polish Statistician

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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.

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Modele Hybrydowe W Prognozowaniu Brakujących Danych W Szeregach O Bardzo Wysokiej Częstotliwości Obserwowania

Cited by 2 publications

References 0 publications

Comparative Analysis of Methods for Hourly Electricity Demand Forecasting in the Absence of Data – A Case Study

Comparative Analysis of Methods for Hourly Electricity Demand Forecasting in the Absence of Data – A Case Study

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

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