Methodology based on spiking neural networks for univariate time-series forecasting

Spiking Neural Networks (SNN) are recognised as well-suited for processing spatiotemporal information with ultra-low energy consumption. However, proposals based on SNN for classification tasks are more common than for forecasting problems. In this sense, this paper presents a new general training m...

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Bibliographic Details
Published in:Neural networks Vol. 173; p. 106171
Main Authors: Lucas, Sergio, Portillo, Eva
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.05.2024
Subjects:
Forecasting
PWM based encoding–decoding algorithm
Spiking Neural Network
Supervised learning
Surrogate gradient
PWM based encoding–decoding algorithm
Surrogate gradient
Spiking Neural Network
Supervised learning
Forecasting
ISSN:0893-6080, 1879-2782, 1879-2782
Online Access:Get full text
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Summary:Spiking Neural Networks (SNN) are recognised as well-suited for processing spatiotemporal information with ultra-low energy consumption. However, proposals based on SNN for classification tasks are more common than for forecasting problems. In this sense, this paper presents a new general training methodology for univariate time-series forecasting based on SNN. The methodology is focused on one-step ahead forecasting problems and combines a PulseWidth Modulation based encoding–decoding algorithm with a Surrogate Gradient method as supervised training algorithm. In order to validate the generality of the presented methodology sine-wave, 3 UCI and 1 available real-world datasets are used. The results show very satisfactory forecasting results (MAE∈[0.0094,0.2891]) regardless of the characteristics of the dataset or the application field. In addition, weights can be initialised just once to achieve robust results, boosting the advantages of computational and energy cost of SNN. •New supervised training methodology for univariate time-series forecasting with SNN.•A PWM based encoding–decoding algorithm and a Surrogate Gradient method are combined.•The methodology is characterised by ultra-low latency and high robustness.•3 UCI datasets and air pollution data for Greater London Area are used for validation.•Satisfactory forecasting results regardless of the characteristics of the dataset.
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ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2024.106171