TimeSeries Scaling
This guide explains how to rescale TimeSeries to a different calendar.
Introduction
Rescaling TimeSeries is the process of changing the changing the calendar of the TimeSeries and fitting the data using that new calendar.
Rescaling from a high frequency calendar to a low frequency calendar, e.g. Daily to Monthly is called aggregation
Rescaling from a low frequency calendar to a high frequency calendar, e.g. Monthly to Daily is called distribution
Aggregation
When you rescale data from a high frequency calendar to a low frequency calendar, we need to reduce or aggregate the data.
The way the data is aggregated is determined by the observed setting.
Observed setting
beginning
An observation made at the beginning of the period
end
An observation made at the end of the period.
This is typically used for end of day settlement prices where the settlement value is either the last traded price or a calculation using the last tradded prices.
averaged
An average of the values observed throughout the period
summed
A sum of the values observed throughout the period
This is typically used for volumes, e.g. the number of traded items in a day, so aggregating to a lower frequency will sum up these values to give you a total volume for the period.
high
The highest value observed throuhout the period
low
The lowest value observed throughout the period
delta
The change of value from the start to the end of the period.
This is typically used for metered data where the values are meter readings which always increase in value. Scaling using delta allows you to see the amount used per period.
none
A point-in-time observation
Implicit observed
When you scale a TimeSeries without specifying the observed option, OpenDataDSL first checks the global observed setting which is set using:
set observed value
If this hasn't been set, it uses the observed attribute of the TimeSeries which defaults to end.
Distribution
When you rescale data from a low frequency calendar to a high frequency calendar, we need to distribute the observed value across a range of values.
The way the data is distributed is determined by the distribution setting.
Distribution setting
constant
The value is constant across the whole period.
This can be seen like a bar chart where the tops of the bars are flat and the value jumps at each source period start.
linear
The values are interpolated using a linear spline between the source values.
This can be seen as a simple line chart where the values are points on the line
cubic
The values are interpolated using a cubic spline between the source values
This can be seen as a smoothed line chart.
none
A single value is used at the source index, the rest of the values are filled in with NaN or Missing
Implicit distribution
When you scale a TimeSeries without specifying the distribution method, OpenDataDSL first checks the global distribution setting which is set using:
set distribution value
If this hasn't been set then constant distribution is used.
Distribution value
The value used to calculate the distributed values is the observed value in the source TimeSeries except for the following cases:
Summed observed
If the TimeSeries is observed as summed then the distributed values are divided by the number of observations so that the sum of those observations matches the original observed value.
Scaling
To rescale a TimeSeries, you using the scale function which has 3 signatures:
ts = scale(input, calendar)
ts = scale(input, calendar, observed)
ts = scale(input, calendar, observed, distribution)
Examples
Monthly to Daily TimeSeries
// Create a monthly TimeSeries
mts = TimeSeries("2021-01-01", MonthlyCalendar(), [1,3,7,5,4,5])
// Scale to daily using a cubic spline
cts = scale(mts, DailyCalendar())
print cts.values
Monthly to Daily TimeSeries - cubic spline
// Create a monthly TimeSeries
mts = TimeSeries("2021-01-01", MonthlyCalendar(), [1,3,7,5,4,5])
// Scale to daily using a cubic spline
cts = scale(mts, DailyCalendar(), "beginning", "cubic")
print cts.values