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Neural Time Series (ANN-TS) Print E-mail
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This module (Artificial Neural Network Time Series – ANN-TS) uses modeling potential of a neural network to predict and forecast future values of a univariate time series. A univariate time series is a series of n measured values yi (i = 1,.., n) assuming  that
  • values yi are measured in regular time intervals (time can be replaced by index without loss of information), or yi describe quantitatively time-sequential units or events without leaving out any from the series.
  • values yi are preferably real-valued (real valued predictions make sense)
  • values yi are in some way related to one or more previous values yi–k , yi = G(yi-1, yi-2, … yi-r) + εi, or, shortly G(i, r) + εi, where r < n is called the depth of the model.
Examples of such a series can be sampled variables in technological processes, periodical outputs from stable or unstable processes, parameters of natural processes in life sciences, geo sciences, physical or chemical sciences, series of financial or economical indices, prices, rates and so on.

PDF Neural Time Series (ANN-TS) - Pdf manual
PDF Neural network - Pdf manual

Dialog window

Output

Protokol:
  • Task name
  • Data
  • Type of model
  • Model depth
  • Forecast length
  • Validation
  • Independent variable
  • Transformation type
  • Dependent variable
  • Transformation type
  • Layer, Neurons
  • Sigmoid steepness
  • Moment
  • Training speed
  • Terminate when error <
  • Training data (%)
  • Termination conditions
  • Optimization report
  • No of iterations
  • Max training error
  • Mean training error
  • Max training error
  • Mean training error
  • Weights
  • Layer / Neuron
  • Relative influence
  • Time series
Grafy:
  • Plot of the time series with ANN-TS prediction and validation or forecast (as selected)
  • Plot of residuals, or differences measured minus predicted including validation data, if selected
  • Graphical representation of the network architecture
  • Relative influence
  • Plot of the training (network optimization) process

Graphical output

Plot of the time series with ANN-TS prediction and validation or forecast (as selected)
Neural Time Series (ANN-TS)

Graphical representation of the network architecture
Neural Time Series (ANN-TS)

Plot of residuals, or differences measured minus predicted including validation data, if selected
Neural Time Series (ANN-TS)

Relative influence
Neural Time Series (ANN-TS)
Plot of the training process
Neural Time Series (ANN-TS)

Last Updated ( 20.03.2013 )
 
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