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Non-dominated solutions for time series learning and forecasting

Generating models with a Generic Two-Phase Pareto Loca Search with VND

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Abstract

While measuring forecasting accuracy has been intensively studied by researchers, the consideration of sets of non-dominated solutions has not yet been explored in the literature. Even when considering measures that are in harmony, small changes in model parameters can result in a trained system capable of forecasting different time series characteristics. In this regard, this study shows how a set of non-dominated forecasting models can assist decision makers to pick the most suitable model for operations on dynamic environments. In particular, the system presents an interesting trade-off provided with assistance of a new strategic measure, which forces the forecasting model to learn the highest values of the time series. An automatic self-adaptive forecasting framework, calibrated with Multi-Objective VND inspired techniques, and able to perform k-steps-ahead forecasting, is considered. There is a growing demand for learning big-data time series, such as those derived from sensors in energy mini/microgrid systems, in which real-time decisions should be made quickly and can vary according to the available energy resources. Thus, a case of study is considered using data from disaggregated power readings of a typical microgrid due to the fact that load demand forecasting, with different components of a house, is of crucial importance for management and operation of new emerging decentralized systems.

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Notes

  1. A search was conducted on different global reference databases such as Web of Science, SCOPUS and IEEEXplore looking up for publications until October, 2020.

  2. Creating Pareto Fronts is a straightforward way to store models that present non-dominated characteristics [29].

  3. HFM code is currently available at https://github.com/vncoelho/HFM.

  4. OptFrame [9, 10] code is currently available at https://github.com/optframe/optframe.

  5. The code can be currently accessed at https://github.com/vncoelho/HFM, commit version with hash fbc2cd3657ad0519235280cd511f582914d8557b.

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  1. OptBlocks, Avenida João Pinheiro, 274 Sala 201 - Lourdes, 30130-186, Belo Horizonte, MG, Brazil

    Vitor Nazário Coelho

  2. Adjunct Faculty, Computer Science Department, University of George Washington, Washington, DC, 20052, USA

    Roozbeh Haghnazar Koochaksaraei

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  1. Vitor Nazário Coelho
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Coelho, V.N., Koochaksaraei, R.H. Non-dominated solutions for time series learning and forecasting. Optim Lett 16, 395–408 (2022). https://doi.org/10.1007/s11590-021-01720-5

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