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

2020 ◽  
Vol 65 (10) ◽  
pp. 24-48
Author(s):  
Jan Zawadzki
Keyword(s):  
Time Series ◽  
Applied Mathematics ◽  
Data Bank ◽  
Hybrid Models ◽  
Seasonal Fluctuations ◽  
University Of Technology ◽  
Hourly Data ◽  
Daily Cycles ◽  
Distribution Company ◽  
Theoretical Considerations

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.

Analyses and anticipating the future trend of accidents in an electricity distribution company of Iran: A time series analysis

Work ◽  
2021 ◽  
pp. 1-6
Author(s):  
Shirin Nasrollah Nejhad ◽  
Tayebeh Ilaghinezhad Bardsiri ◽  
Maryam feiz arefi ◽  
Amin babaei poya ◽  
Ehsan mazloumi ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Occupational Injuries ◽  
Model Management ◽  
High Rate ◽  
Electricity Distribution ◽  
Temporary Workers ◽  
Series Analysis ◽  
The Future ◽  
Distribution Company

BACKGROUND: Many work-related fatalities happen every year in electricity distribution companies. This study was conducted to model accidents using the time series analysis and survey descriptive factors of injuries in an electricity distribution company in Tehran, Iran. METHODS: Data related to 2010 to 2017 were collected from the database of the safety department. Time Series and trend analysis were used for data analyzing and anticipating the accidents up to 2022. RESULT: Most of the accidents occurred in summer. Workers’ negligence was the reason for 75%of deaths. Employment type and type of injuries had a significant relationship (p <  0.05). CONCLUSION: The anticipating model indicated occupational injuries are going to have an increase in the future. A high rate of accidents in summer maybe because of the warm weather or insufficient skills in temporary workers. Temporary workers have no chance to work in a year like permanent workers, therefore acquisition experiences may be less in them. Based on the estimating model, Management should pay attention to those sectors of the company where most of the risky activities take place. Also, training programs and using personal protective equipment can help to protect workers in hazardous conditions.

scholarly journals Literasi Keuangan Dimasa Pandemi Covid 19 (FLC19) dan Pengaruhnya Terhadap Siklus Keuangan Di Indonesia

POINT ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 21-33
Author(s):  
Edwin Basmar ◽  
Hasdiana S
Keyword(s):  
Time Series ◽  
Data Bank

Literasi keuangan dan Pandemi Covid 19 merupakan dua kondisi yang saling bertentangan, sehingga memberikan pengaruh pada pergerakan siklus keuangan di Indonesia, proses pengukuran tekanan literasi keuangan di masa Pandemi Covid 19 (FLC19) ini, dilakukan dengan menggunakan data Bank Indonesia selama priode Pandemi Covid 19 (2019 - 2021) secara time series, dengan menggunakan model pengembangan Ed Waves Index, hasil penelitian ini menemukan bahwa, untuk tipe tekanan literasi keuangan positif (FLC19+) bertekanan 0.015 Amplitudo yang menandakan adanya pertumbuhan perekonomian, sementara untuk tipe tekanan literasi keuangan negatif (FLC19-) bertekanan -0.024 Amplitudo yang menandakan ketidakstabilan keuangan dalam perekonomian Indonesia selama Pandemi Covid 19.

scholarly journals Research on Computer Modeling of Fractional Differential Equation Applied Mathematics

2021 ◽  
Vol 2083 (3) ◽  
pp. 032036
Author(s):  
Linlin Su
Keyword(s):  
Differential Equation ◽  
Time Series ◽  
Hopf Bifurcation ◽  
Equilibrium Solution ◽  
Applied Mathematics ◽  
Bifurcation Diagrams ◽  
Financial Systems ◽  
Fractional Differential ◽  
The Stability ◽  
Evolution Behavior

Abstract This paper qualitatively analyzes the stability of the equilibrium solution of a class of fractional chaotic financial systems and the conditions for the occurrence of Hopf bifurcation, and uses the Adams-Bashford-Melton predictive-correction finite difference method to pass the analysis Bifurcation diagrams, phase diagrams, and time series diagrams are used to simulate the complex evolution behavior of the system.

scholarly journals Design of Seasonal Adjustment Filter Robust to Variations in the Seasonal Behaviour of Time Series

2017 ◽  
Vol 33 (1) ◽  
pp. 155-186
Author(s):  
Marcela Cohen Martelotte ◽  
Reinaldo Castro Souza ◽  
Eduardo Antônio Barros da Silva
Keyword(s):  
Time Series ◽  
Good Alternative ◽  
Superior Performance ◽  
Seasonal Adjustment ◽  
Artificial Data ◽  
Seasonal Fluctuations ◽  
Seasonal Behaviour ◽  
Macroeconomic Time Series ◽  
The One ◽  
Made In

Abstract Considering that many macroeconomic time series present changing seasonal behaviour, there is a need for filters that are robust to such changes. This article proposes a method to design seasonal filters that address this problem. The design was made in the frequency domain to estimate seasonal fluctuations that are spread around specific bands of frequencies. We assessed the generated filters by applying them to artificial data with known seasonal behaviour based on the ones of the real macroeconomic series, and we compared their performance with the one of X-13A-S. The results have shown that the designed filters have superior performance for series with pronounced moving seasonality, being a good alternative in these cases.

Innovative hybrid models for forecasting time series applied in wind generation based on the combination of time series models with artificial neural networks

Energy ◽  
2018 ◽  
Vol 151 ◽  
pp. 347-357 ◽  
Author(s):  
Henrique do Nascimento Camelo ◽  
Paulo Sérgio Lucio ◽  
João Bosco Verçosa Leal Junior ◽  
Paulo Cesar Marques de Carvalho ◽  
Daniel von Glehn dos Santos
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Hybrid Models ◽  
Wind Generation ◽  
Time Series Models ◽  
Artificial Neural

Load Forecasting using Time Series Techniques

2021 ◽  
pp. 11343-11357
Author(s):  
Shahida Khatoon, Ibraheem, Priti, Mohammad Shahid
Keyword(s):  
Time Series ◽  
Linear Models ◽  
Linear Trend ◽  
Moving Average ◽  
Load Forecasting ◽  
Electric Load ◽  
Time Data ◽  
Efficient Operation ◽  
Use Of Time ◽  
Distribution Company

Load Forecasting is of great significance for effective and efficient operation of power system. Use of time series is of much importance in load forecasting. In this study, effectiveness of different time series techniques is identified to gathered valuable information. The objective is to predict electric load efficiently and effectively. This paper analyses the prediction accuracy of variety of time series method in modeling Electric load forecasts. The study examines the time series forecasting methods applied to estimate future electric load, specifically, Moving Average (MA), Linear Trend, the Exponential and Parabolic Trend. A comparison of different forecasting techniques of Time Series is demonstrated on real time data. The data utilized for forecast is made available through a distribution company of India. The traditional linear models and hybrid models along with ANN are developed. These models are appraised for the forecasting capability.

scholarly journals Comparison of GPS tropospheric delays derived from two consecutive EPN reprocessing campaigns from the point of view of climate monitoring

2016 ◽  
Vol 9 (9) ◽  
pp. 4861-4877 ◽  
Author(s):  
Zofia Baldysz ◽  
Grzegorz Nykiel ◽  
Andrzej Araszkiewicz ◽  
Mariusz Figurski ◽  
Karolina Szafranek
Keyword(s):  
Time Series ◽  
Point Of View ◽  
Trend Test ◽  
Data Sets ◽  
Total Delay ◽  
University Of Technology ◽  
Time Period ◽  
Seasonal Signals ◽  
Linear Trends

Abstract. The main purpose of this research was to acquire information about consistency of ZTD (zenith total delay) linear trends and seasonal components between two consecutive GPS reprocessing campaigns. The analysis concerned two sets of the ZTD time series which were estimated during EUREF (Reference Frame Sub-Commission for Europe) EPN (Permanent Network) reprocessing campaigns according to 2008 and 2015 MUT AC (Military University of Technology Analysis Centre) scenarios. Firstly, Lomb–Scargle periodograms were generated for 57 EPN stations to obtain a characterisation of oscillations occurring in the ZTD time series. Then, the values of seasonal components and linear trends were estimated using the LSE (least squares estimation) approach. The Mann–Kendall trend test was also carried out to verify the presence of linear long-term ZTD changes. Finally, differences in seasonal signals and linear trends between these two data sets were investigated. All these analyses were conducted for the ZTD time series of two lengths: a shortened 16-year series and a full 18-year one. In the case of spectral analysis, amplitudes of the annual and semi-annual periods were almost exactly the same for both reprocessing campaigns. Exceptions were found for only a few stations and they did not exceed 1 mm. The estimated trends were also similar. However, for the reprocessing performed in 2008, the trends values were usually higher. In general, shortening of the analysed time period by 2 years resulted in a decrease of the linear trends values of about 0.07 mm yr−1. This was confirmed by analyses based on two data sets.

scholarly journals Machine prediction of US imports from the PRC in the context of mutual sanctions

2020 ◽  
Vol 73 ◽  
pp. 01027
Author(s):  
Petr Šuleř ◽  
Jan Mareček
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
United States Of America ◽  
The United States ◽  
Seasonal Fluctuations ◽  
Trade Restrictions ◽  
Republic Of China ◽  
The Usa ◽  
Multilayer Perceptron Networks ◽  
Excellent Tool

The aim of this paper is to mechanically predict the import of the United States of America (USA) from the People's Republic of China (PRC). The trade restrictions of the USA and the PRC caused by the USA feeling of imbalance of trade between the two states have significantly influenced not only the trade between the two players, but also the overall climate of international trade. The result of this paper is the finding that multilayer perceptron networks (MLP) appear to be an excellent tool for predicting USA imports from the PRC. MLP networks can capture both the trend of the entire time series and its seasonal fluctuations. It also emerged that time series delays need to be applied. Acceptable results are shown to delay series of the order of 5 and 10 months. The mutual sanctions of both countries did not have a significant impact on the outcome of the machine learning prediction.

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