This class describes one unit of input, or output, to a temporal neural network. Data can be both an input and output. Inputs are used to attempt predict the output.

An error occured related to the temporal data set.

This class implements a temporal neural data set. A temporal neural dataset is designed to use a neural network to predict. A temporal dataset is a stream of data over a time range. This time range is broken up into "points". Each point can contain one or more values. These values are either the values that you would like to predict, or use to predict. It is possible for a value to be both predicted and used to predict. For example, if you were trying to predict a trend in a stock's price fluctuations you might very well use the security price for both. Each point that we have data for is stored in the TemporalPoint class. Each TemporalPoint will contain one more data values. These data values are described by the TemporalDataDescription class. For example, if you had five TemporalDataDescription objects added to this class, each Temporal point object would contain five values. Points are arranged by sequence number. No two points can have the same sequence numbers. Methods are provided to allow you to add points using the Date class. These dates are resolved to sequence number using the level of granularity specified for this class. No two points can occupy the same granularity increment.

A point in tme for a temporal data set.

The type of data requested.