RT Journal Article
T1 A novel distributed forecasting method based on information fusion and incremental learning for streaming time series
A1 Melgar García, Laura
A1 Gutiérrez-Avilés, David
A1 Rubio-Escudero, Cristina
A1 Troncoso, Alicia
K1 Real-time forecasting
K1 Incremental learning
K1 Streaming time series
K1 Electricity demand
AB Real-time algorithms have to adapt and adjust to new incoming patterns to provide timely and accurate responses. This paper presents a new distributed forecasting algorithm for streaming time series called StreamWNN. StreamWNN starts with an offline stage in which a forecasting model based on tuples of information fusion is created with historical data. In particular, this model consists of the fusion of patterns composed of past values of the time series with the future values of their k-nearest neighbors. Afterwards, streaming data starts to arrive. The model is incrementally updated in the online stage using a buffer with streaming data that more accurately matches the current model patterns. The model can be updated daily, monthly, quarterly or based on error thresholds. The methodology has been applied to Spanish electricity demand time series providing more accurate results when the model is updated incrementally. The best error results are obtained with the daily update of the model, resulting in an error between 2% and 3.5% depending on the prediction horizon. The model provides better error results than other algorithms.
PB Elsevier
YR 2023
FD 2023-07-21
LK https://hdl.handle.net/10433/25791
UL https://hdl.handle.net/10433/25791
LA en
NO Information Fusion, vol. 95, p. 163-173
NO Data Science & Big Data Lab
DS RIO
RD May 30, 2026