This module (Artificial Neural Network Time Series – ANNTS) 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 y_{i} (i = 1,.., n) assuming that
 values y_{i} are measured in regular time intervals (time can be replaced by index without loss of information), or y_{i} describe quantitatively timesequential units or events without leaving out any from the series.
 values y_{i} are preferably realvalued (real valued predictions make sense)
 values y_{i} are in some way related to one or more previous values y_{i–k} , y_{i} = G(y_{i1}, y_{i2}, … y_{ir}) + ε_{i}, or, shortly G(i, r) + ε_{i}, 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.
Neural Time Series (ANNTS)  Pdf manual
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 ANNTS 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 ANNTS prediction and validation or forecast (as selected)
Graphical representation of the network architecture
Plot of residuals, or differences measured minus predicted including validation data, if selected
Relative influence

Plot of the training process

