An up-to-date and accurate discussion of spiking neural P systems in time series analysis In Spiking Neural P Systems for Time Series Analysis, the authors explore the fundamentals and the current states of both spiking neural P systems and time series analysis and examine the application models of time series analysis. You’ll also find walkthroughs of recurrent-like, echo-like, and reservoir computing models for time series prediction. The book covers applications in time series analysis such as financial time series analysis, power load forecasting, photovoltaic power forecasting, and medical signal processing, and contains illustrative photographs and tables designed to improve reader understanding. Readers will also find: A thorough introduction to the theoretical and application research relevant to membrane computing and spiking P neural systems Comprehensive explorations of a variety of recurrent-like models for time series forecasting, including LSTM-SNP and GSNP models Practical discussions of common problems in reservoir computing models, including classification problems Complete evaluations of models used in financial time series analysis, power load forecasting, and other techniques Perfect for scientists, researchers, postgraduates, lecturers, and teachers, Spiking Neural P Systems for Time Series Analysis will also benefit undergraduate students interested in advanced techniques for time series analysis.
