@inproceedings{ddfcd300253e441986bafe5e52b7235f,
title = "Forecasting weather signals using a polychronous spiking neural network",
abstract = "Due to its inherently complex and chaotic nature predicting various weather phenomena over non trivial periods of time is extremely difficult. In this paper, we consider the ability of an emerging class of temporally encoded neural network to address the challenge of weather forecasting. The Polychronous Spiking Neural Network (PSNN) uses axonal delay to encode temporal information into the network in order to make predictions about weather signals. The performance of this network is benchmarked against the Multi-Layer Perceptron network as well as Linear Predictor. The results indicate that the inherent characteristics of the Polychronous Spiking Network make it well suited to the processing and prediction of complex weather signals.",
keywords = "Axonal delay, Natural event forecasting, Spiking neural network, Weather time series",
author = "David Reid and Hissam Tawfik and Hussain, \{Abir Jaafar\} and Haya Al-Askar",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 11th International Conference on Intelligent Computing, ICIC 2015 ; Conference date: 20-08-2015 Through 23-08-2015",
year = "2015",
doi = "10.1007/978-3-319-22180-9\_12",
language = "English",
isbn = "9783319221793",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "116--123",
editor = "Vitoantonio Bevilacqua and De-Shuang Huang and Prashan Premaratne",
booktitle = "Intelligent Computing Theories and Methodologies - 11th International Conference, ICIC 2015, Proceedings",
address = "Germany",
}