| TimeSeries.adjust_endpoints | |
| TimeSeries.compressed | |
| TimeSeries.fill_missing_dates |
| adjust_endpoints | |
| align_series | |
| aligned |
Rates of change or differences in a TimeSeries can be calculated easily using the tshift method.
As an illustration, let us suppose we need to calculate a term-to-term return rate for a monthly series mser. We could initially try something along the lines of
>>> mser = ts.time_series(np.arange(1., 13.),
... start_date=ts.Date('M','2001-01'))
>>> tot_change = 100 * (mser[3:]/mser[:-3] - 1)
>>> tot_change
masked_array(data = [ 300. 150. 100.
75. 60. 50.
42.85714286 37.5 33.33333333],
mask = False,
fill_value = 1e+20)
This gives the correct numerical result, but as mser[3:] and mser[:-3] have different starting and ending dates, the result is forced to a plain numpy.ma.MaskedArray. Moreover, yoy_change does not have the same size as the original input series, which may be inconvenient.
A solution consists in using the tshift method.
>>> yoy_change = 100 * (mser/mser.tshift(-3, copy=False) - 1)
The command mser.tshift(-3, copy=False) returns a series with the same dates as mser, but with values shifted 3 periods to the right. The first 3 values of the resulting series are masked.
Note
By default, the tshift copies the original series. For simpler cases as the one above, a copy may be avoided by using the copy=False.
| TimeSeries.tshift |