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.
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References
Mathur, P.S., Kumar, A., Chandra, M.: A feature based neural network model for weather forecasting. In: World Academy of Science, Engineering and Technology (2007)
Box, G.E.P., Jenkins, G.W., Reinsel, G.C.: Time Series Analysis - Forecasting and Control, 3rd edn. Prentice-Hall, Englewood Cliffs (1994)
Sfetsos, A.: A comparison of various forecasting techniques applied to mean hourly wind speed time series. Renewable Energy 21(1), 23–35 (2000)
Ho, S., Xie, M., Goh, T.N.: A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction. Comput. Ind. Eng. 42(2–4), 371–375 (2002)
Faraway, J., Chatfield, C.: Time series forecasting with neural networks: a comparative study using the airline data. J. R. Stat. Soc. 47(2), 231–250 (1998)
Zhang, G.P., Patuwo, B.E., Hu, M.Y.: A simulation study of artificial neural networks for nonlinear time-series forecasting. Comput. Oper. Res. 28, 381–396 (2001)
Hodgkin, A., Huxley, A.: A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117, 500–544 (1952)
Abeles, M.: Corticonics: Neural circuits of the cerebral cortex. Cambridge University Press, New York (1991)
Maass, W.: Networks of spiking neurons the third generation of neural network models. Neural Netw. 10(9), 1659–1671 (1997). Elsevier Publishing
Maass, W., Bishop, C.M.: Pulsed Neural Networks. MIT Press, Cambridge (1998). ISBN 0-262-13350-4
Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans. Neural Netw. 14(6), 1569–1572 (2003)
Legenstein, R., Naeger, C., Maass, W.: What can a neuron learn with spike-timing-dependent-plasticty. J. Neural Comput. 17(11), 2337–2382 (2005)
Nessler, B., Pfeiffer, M., Maass, W.: STDP enables spiking neurons to detect hidden causes of their inputs. In: Proceedings of NIPS 2009: Advances in Neural Information Processing Systems, vol. 22, pp. 1357–1365. MIT Press (2010)
Levy, W.B., Steward, O.: Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus. Neuroscience 8(4), 791–797 (1983)
Markram, H., Lübke, J., Frotscher, M., Sakmann, B.: Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275(5297), 213–215 (1997)
Izhikevich, E.M.: Polychronization: computation with spikes. J. Neural Comput. 18(2), 245–282 (2006)
Arthur, J.V., Boahen, K.A.: Synchrony in Silicon The Gamma Rhythm. IEEE Trans. Neural Netw. 18(6), 1815–1825 (2007)
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Reid, D., Tawfik, H., Hussain, A.J., Al-Askar, H. (2015). Forecasting Weather Signals Using a Polychronous Spiking Neural Network. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_12
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DOI: https://doi.org/10.1007/978-3-319-22180-9_12
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