Forecasting without context problem

Ortiz-Bejar, José, Ortiz-Bejar, Jesus, Zamora-Mendez, Alejandro, Pineda-Garcia, Garibaldi, Graff, Mario and Tellez, Eric S (2020) Forecasting without context problem. 2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), Ixtapa, Mexico, 4-6 November 2020. Published in: IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). 4 1-6. IEEE

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Abstract

This work presents an analysis of four regression systems. Two of them are statistical: the widely used Auto-regressive Integrated Moving Average (ARIMA) and the state-of-the-art Facebook Prophet. From the deep learning school, a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) is also evaluated. We finish our quartet with a fine-tuned Nearest Neighbor model. The study is carried out over seventeen benchmarks; fifteen coming from M4-Competition and two more power systems time series, i.e., electricity demand and hydropower generation. For all the models, the regression systems are fitted and optimized to minimize user intervention. The results show that deep learning models obtained the best performance; nonetheless, the performance difference is not statistically significant with the rest of the systems tested.

Item Type: Conference Proceedings
Keywords: Time series analysis, Training, Power systems, Predictive models, Forecasting, Benchmark testing, Recurrent neural networks
Schools and Departments: School of Engineering and Informatics > Informatics
Research Centres and Groups: Centre for Computational Neuroscience and Robotics
Subjects: Q Science > Q Science (General) > Q0300 Cybernetics > Q0325 Self-organizing systems. Conscious automata > Q0325.5 Machine learning
Q Science > QA Mathematics > QA0075 Electronic computers. Computer science > QA0076.87 Neural computers. Neural networks
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK0275 Electric standards and measurements
Depositing User: Garibaldi Pineda Garcia
Date Deposited: 02 Dec 2020 11:50
Last Modified: 02 Dec 2020 11:51
URI: http://sro.sussex.ac.uk/id/eprint/95449

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