Ponencia
Nearest Neighbors-Based Forecasting for Electricity Demand Time Series in Streaming
Autor/es | Melgar García, Laura
Gutiérrez Avilés, David Rubio Escudero, Cristina Troncoso Lora, Alicia |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2021 |
Fecha de depósito | 2022-04-06 |
Publicado en |
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ISBN/ISSN | 978-3-030-85712-7 0302-9743 |
Resumen | This paper presents a new forecasting algorithm for time series in streaming
named StreamWNN. The methodology has two well-differentiated stages: the algorithm
searches for the nearest neighbors to generate an initial ... This paper presents a new forecasting algorithm for time series in streaming named StreamWNN. The methodology has two well-differentiated stages: the algorithm searches for the nearest neighbors to generate an initial prediction model in the batch phase. Then, an online phase is carried out when the time series arrives in streaming. In par-ticular, the nearest neighbor of the streaming data from the training set is computed and the nearest neighbors, previously computed in the batch phase, of this nearest neighbor are used to obtain the predictions. Results using the electricity consumption time series are reported, show-ing a remarkable performance of the proposed algorithm in terms of fore-casting errors when compared to a nearest neighbors-based benchmark algorithm. The running times for the predictions are also remarkable |
Agencias financiadoras | Ministerio de Ciencia, Innovación y Universidades (MICINN). España |
Identificador del proyecto | TIN2017-88209-C2 |
Cita | Melgar García, L., Gutiérrez Avilés, D., Rubio Escudero, C. y Troncoso, A. (2021). Nearest Neighbors-Based Forecasting for Electricity Demand Time Series in Streaming. En CAEPIA 2021: 19th Conference of the Spanish Association for Artificial Intelligence (185-195), Málaga, España: Springer. |
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