Returns a TimeSeries where each time series is differenced with order 1.
Returns a TimeSeries where each time series is differenced with order 1. The new TimeSeries will be missing the first date-time.
Returns a TimeSeries where each time series is differenced with the given order.
Returns a TimeSeries where each time series is differenced with the given order. The new TimeSeries will be missing the first n date-times.
Gets the first univariate series and its key.
This is equivalent to lags(lagsPerCol, TimeSeries.
This is equivalent to lags(lagsPerCol, TimeSeries.laggedPairKey _). It returns TimeSeries with a new key that is a pair of (original key, lag order).
IMPORTANT: this function assumes that the DateTimeIndex is a UniformDateTimeIndex, not an Irregular one.
IMPORTANT: this function assumes that the DateTimeIndex is a UniformDateTimeIndex, not an Irregular one.
Lags the specified individual time series of the TimeSeries instance by up to their matching lag amount. Each time series can be indicated to either retain the original value, or drop it.
In other words, the lagsPerCol has the following structure:
("variableName1" -> (keepOriginalValue, maxLag), "variableName2" -> (keepOriginalValue, maxLag), ...)
See description of the above lags function for an example of the lagging process.
This is equivalent to lags(maxLag, includeOriginals, TimeSeries.
This is equivalent to lags(maxLag, includeOriginals, TimeSeries.laggedPairKey _). It returns TimeSeries with a new key that is a pair of (original key, lag order).
IMPORTANT: this function assumes that the DateTimeIndex is a UniformDateTimeIndex, not an Irregular one.
IMPORTANT: this function assumes that the DateTimeIndex is a UniformDateTimeIndex, not an Irregular one.
Lags all individual time series of the TimeSeries instance by up to maxLag amount. The lagged time series has its keys generated by the laggedKey function which takes two input parameters: the original key and the lag order, and should return a corresponding lagged key.
Example input TimeSeries: time a b 4 pm 1 6 5 pm 2 7 6 pm 3 8 7 pm 4 9 8 pm 5 10
With maxLag 2, includeOriginals = true and TimeSeries.laggedStringKey, we would get: time a lag1(a) lag2(a) b lag1(b) lag2(b) 6 pm 3 2 1 8 7 6 7 pm 4 3 2 9 8 7 8 pm 5 4 3 10 9 8
Applies a transformation to each series such that the resulting series align with the given time index.
Applies a transformation to each series that preserves the time index.
Applies a transformation to each series that preserves the time index.
Applies a transformation to each series that preserves the time index. Passes the key along with each series.
Returns a return series for each time series.
Returns a return series for each time series. Assumes periodic (as opposed to continuously compounded) returns.
Returns a TimeSeries where each time series is quotiented with order 1.
Returns a TimeSeries where each time series is quotiented with order 1. The new TimeSeries will be missing the first date-time.
Returns a TimeSeries where each time series is quotiented with the given order.
Returns a TimeSeries where each time series is quotiented with the given order. The new TimeSeries will be missing the first n date-times.