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Mirroring our thought processes [recurrent neural network and time series in forecasting]

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1 Author(s)
Shaun-Inn Wu ; California State Univ., San Marcos, CA, USA

To employ simple exponential smoothing in statistical forecasting, we essentially have to assume that the time series fluctuates at a gradually changing mean level. Forecasts are created on an iterative basis by weighing averages of observed values in the time series. The weights are assigned unequally with heavier weights applied to the most recent observations and exponentially declining weights to observations made far in the past. Yet, simple exponential smoothing still cannot help in making accurate predictions. One still has to monitor this forecasting system to determine whether or not the weights need to be adjusted to reduce forecasting errors. Since artificial neural network (ANN) technology provides us with weight adjusting algorithms, we propose using a special ANN architecture, a simple recurrent neural network. This network will provide a simple exponential smoothing forecasting system with an adaptive weighting scheme

Published in:

IEEE Potentials  (Volume:14 ,  Issue: 5 )