This study presents a time series prediction model with output self feedback which is implemented
based on online sequential extreme learning machine. The output variables derived from multilayer perception
can feedback to the network input layer to create a temporal relation between the current node inputs and the
lagged node outputs while overcoming the limitation of memory which is a vital part for any time-series prediction
application. The model can overcome the static prediction problem with most time series prediction models
and can effectively cope with the dynamic properties of time series data. A linear and a nonlinear forecasting
algorithms based on online extreme learning machine are proposed to implement the output feedback forecasting
model. They are both recursive estimator and have two distinct phases: Predict and Update. The proposed
model was tested against different kinds of time series data and the results indicate that the model outperforms
the original static model without feedback.
PAN Feng1* (潘 峰), ZHAO Hai-bo2 (赵海波)
. Online Sequential Extreme Learning Machine Based Multilayer Perception with Output Self Feedback for Time Series Prediction[J]. Journal of Shanghai Jiaotong University(Science), 2013
, 18(3)
: 366
-375
.
DOI: 10.1007/s12204-013-1407-0
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