In several domains as statistics, signal processing or econometrics, a time series is a sequence of data points, measured typically at successive times spaced at uniform time intervals. Time series data have a natural temporal ordering. This makes time series analysis distinct from other common data analysis problems, in which there is no natural ordering of the observations. A time series model will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for a given period will be expressed as deriving in some way from past values, rather than from future values.

The data sets shown in this section represents time series problems. Consequently, the output value of these data sets is real valued.

Each data file has the following structure:

• @relation: Name of the data set
• @attribute: Description of an attribute (one for each attribute)
• @inputs: List with the names of the input attributes
• @output: List with the names of the output attributes
• @data: Starting tag of the data

The rest of the file contains all the examples belonging to the data set, expressed in comma sepparated values format.

In the data sets, the first attribute (TimeStamp) does not appear in the @inputs. This is because that attribute is used to determine the position of each point in the series, but the model should not use it to predict the output value.