Temporal operators in Trevas
The version 1.4.1 of Trevas introduces preliminary support for date and time types
and operators.
The specification describes temporal types such as date, time_period, time, and duration. However, Trevas authors find
these descriptions unsatisfactory. This blog post outlines our implementation choices and how they differ from the spec.
In the specification, time_period (and the types date) is described as a compound type with a start and end (or a
start and a duration). This complicates the implementation and brings little value to the language as one can simply
operate on a combination of dates or date and duration directly. For this reason, we defined an algebra between the
temporal types and did not yet implement the time_period.
| result (operators) | date | duration | number |
|---|
| date | n/a | date (+, -) | n/a |
| duration | date (+, -) | duration (+, -) | duration (*) |
| number | n/a | duration (*) | n/a |
The period_indicator function relies on period-awareness for types that are not defined enough at the moment to
be implemented.
Java mapping
The VTL type date is represented internally as the
types java.time.Instant,
java.time.ZonedDateTime
and java.time.OffsetDateTime
Instant represent a specific moment in time. Note that this type does not include timezone information and is therefore
not usable with all the operators. One can use the types ZonedDateTime and OffsetDateTime when timezone or time
saving is required.
The VTL type duration is represented internally as the
type org.threeten.extra.PeriodDuration
from the threeten extra package.
It represents a duration using both calendar units (years, months, days) and a temporal amount (hours, minutes, seconds
and nanoseconds).
Function flow_to_stock
The flow_to_stock function converts a data set with flow interpretation into a stock interpretation. This transformation
is useful when you want to aggregate flow data (e.g., sales or production rates) into cumulative stock data (e.g., total
inventory).
Syntax:
result := flow_to_stock(op)
Parameters:
op - The input data set with flow interpretation. The data set must have an identifier of type time, additional
identifiers, and at least one measure of type number.
Result:
The function returns a data set with the same structure as the input, but with the values converted to stock
interpretation.
Function stock_to_flow
The stock_to_flow function converts a data set with stock interpretation into a flow interpretation. This
transformation is useful when you want to derive flow data from cumulative stock data.
Syntax:
result := stock_to_flow(op)
Parameters:
op - The input data set with stock interpretation. The data set must have an identifier of type time, additional
identifiers, and at least one measure of type number.
Result:
The function returns a data set with the same structure as the input, but with the values converted to flow
interpretation.
Function timeshift
The timeshift function shifts the time component of a specified range of time in the data set. This is useful for
analyzing data at different time offsets, such as comparing current values to past values.
Syntax:
result := timeshift(op, shiftNumber)
Parameters:
op - The operand data set containing time series.shiftNumber - An integer representing the number of periods to shift. Positive values shift forward in time, while
negative values shift backward.
Result:
The function returns a data set with the time identifiers shifted by the specified number of periods.
