Time Series node - observations

Observations that are specified by a date/time field

You can specify that the observations are defined by a date, time, or timestamp field. In addition to the field that defines the observations, select the appropriate time interval that describes the observations. Depending on the specified time interval, you can also specify other settings, such as the interval between observations (increment) or the number of days per week. The following considerations apply to the time interval:

• Use the value Irregular when the observations are irregularly spaced in time, such as the time at which a sales order is processed. When Irregular is selected, you must specify the time interval that is used for the analysis, from the Time Interval settings on the Data Specifications tab.
• When the observations represent a date and time and the time interval is hours, minutes, or seconds, then use Hours per day, Minutes per day, or Seconds per day. When the observations represent a time (duration) without reference to a date and the time interval is hours, minutes, or seconds, then use Hours (non-periodic), Minutes (non-periodic), or Seconds (non-periodic).
• Based on the selected time interval, the procedure can detect missing observations. Detecting missing observations is necessary since the procedure assumes that all observations are equally spaced in time and that no observations are missing. For example, if the time interval is Days and the date 2015-10-27 is followed by 2015-10-29, then an observation is missing for 2015-10-28. Values are imputed for any missing observations; use the Missing Value Handling area of the Data Specifications tab to specify settings for handling missing values.
• The specified time interval allows the procedure to detect multiple observations in the same time interval that need to be aggregated together and to align observations on an interval boundary, such as the first of the month, to ensure that the observations are equally spaced. For example, if the time interval is Months, then multiple dates in the same month are aggregated together. This type of aggregation is referred to as grouping. By default, observations are summed when grouped. You can specify a different method for grouping, such as the mean of the observations, from the Aggregation and Distribution settings on the Data Specifications tab.
• For some time intervals, the additional settings can define breaks in the normal equally spaced intervals. For example, if the time interval is Days, but only weekdays are valid, you can specify that there are five days in a week, and the week begins on Monday.

Observations that are defined as periods or cyclic periods

Observations can be defined by one or more integer fields that represent periods or repeating cycles of periods, up to an arbitrary number of cycle levels. With this structure, you can describe series of observations that don't fit one of the standard time intervals. For example, a fiscal year with only 10 months can be described with a cycle field that represents years and a period field that represents months, where the length of one cycle is 10.

Fields that specify cyclic periods define a hierarchy of periodic levels, where the lowest level is defined by the Period field. The next highest level is specified by a cycle field whose level is 1, followed by a cycle field whose level is 2, and so on. Field values for each level, except the highest, must be periodic with respect to the next highest level. Values for the highest level cannot be periodic. For example, in the case of the 10-month fiscal year, months are periodic within years and years are not periodic.

• The length of a cycle at a particular level is the periodicity of the next lowest level. For the fiscal year example, there is only one cycle level and the cycle length is 10 since the next lowest level represents months and there are 10 months in the specified fiscal year.
• Specify the starting value for any periodic field that does not start from 1. This setting is necessary for detecting missing values. For example, if a periodic field starts from 2 but the starting value is specified as 1, then the procedure assumes that there is a missing value for the first period in each cycle of that field.