One way to contrast the behaviour of different algorithms in the field of timeseries forecasting is to compare the prediction error using a benchmark problem. Another interesting ...
Time series data is common in many settings including scientific and financial applications. In these applications, the amount of data is often very large. We seek to support pred...
: A new supervised learning procedure for training RBF networks is proposed. It uses a pair of parallel running Kalman filters to sequentially update both the output weights and th...
Kohonen self-organisation maps are a well know classification tool, commonly used in a wide variety of problems, but with limited applications in time series forecasting context....
Geoffroy Simon, Amaury Lendasse, Marie Cottrell, J...
Firstly, a method is introduced which uses Volterra series deploying technique to construct a nonlinear model based on OFS model. Then an improved novel incremental mode multiple s...
Haitao Zhang, Zonghai Chen, Ming Li, Wei Xiang, Ti...