1. Time Series Forecasting with Recurrent Neural Networks NN3 Competition Mahmoud Abou-Nasr Research & Advanced Engineering Ford Motor Company

2. Software
NTOOL Software Package developed in FORD, used for training the networks.

3. RMLP Architecture Typically 1-4R-2-1L One input node
Four fully recurrent nonlinear (bipolar sigmoid) nodes in the first hidden layer
2 nonlinear nodes in the second hidden layer
One linear output node

4. Training Details EKF multi-stream training, with typically 25 streams.
Each trajectory/stream is of length P, where P is no longer than half the number of points N in the series.
The input for training the network is taken from the actual series for P-M points, and from the network output for the last M points (M is the number of points to be predicted).
Switching logic is internal to the network.
Typically training time: 2 minutes per network
MSE error function.

5. P varies depending on the length of the series.
For a short series: P is 35 or about 0.5 N
For a long series: P is 60 or about 0.4 N

6. First P-M Training Steps
From Actual Series
RMLP
From Network Output
Last M Training Steps
RMLP

7. Typical Training Stream For a Long Series
P-M=42
M=18
P=60
End
Start
N = 143
Typical Training Stream For a Long Series

8. Ensemble of Networks
Maximum of ten networks of the same architecture were used to form an ensemble .
The trained networks were embedded in one architecture, with an output averaging node.
The networks used in the ensemble were the only networks trained. (They were not selected from a larger universe of trained networks).