def test_fill_constant(self):
seriesA: TimeSeries = TimeSeries.from_times_and_values(
self.time,
np.array([np.nan] * 5 + [2.0] * 5 + [np.nan] * 5 + [2.0] * 10 +
[np.nan] * 5),
)
# Check that no changes are made if there are no missing values
self.assertEqual(self.series1,
fill_missing_values(self.series1, "auto"))
# Check that a constant function is filled to a constant function
self.assertEqual(self.series1, fill_missing_values(seriesA, "auto"))
def test_linear(self):
seriesB: TimeSeries = TimeSeries.from_times_and_values(
self.time,
np.array(self.lin[:10] + [np.nan] * 10 + self.lin[-10:]))
# Check for linear interpolation part
self.assertEqual(self.series2, fill_missing_values(seriesB, "auto"))
def test_fill_quad(self):
seriesE: TimeSeries = TimeSeries.from_times_and_values(
self.time,
np.array(self.cub[:10] + [np.nan] * 10 + self.cub[-10:]))
self.assertEqual(
self.series5,
round(fill_missing_values(seriesE, "auto", method="quadratic"), 7),
)
def fit(self, series: TimeSeries):
series = fill_missing_values(series)
super().fit(series)
series = self.training_series
# determine trend
if self.trend == "poly":
trend_coefficients = np.polyfit(range(len(series)),
series.univariate_values(),
self.trend_poly_degree)
self.trend_function = np.poly1d(trend_coefficients)
elif self.trend == "exp":
trend_coefficients = np.polyfit(range(len(series)),
np.log(series.univariate_values()),
1)
self.trend_function = lambda x: np.exp(trend_coefficients[
1]) * np.exp(trend_coefficients[0] * x)
else:
self.trend_function = lambda x: 0
# subtract trend
detrended_values = series.univariate_values() - self.trend_function(
range(len(series)))
detrended_series = TimeSeries.from_times_and_values(
series.time_index, detrended_values)
# crop training set to match the seasonality of the first prediction point
if self.required_matches is None:
curr_required_matches = _find_relevant_timestamp_attributes(
detrended_series)
else:
curr_required_matches = self.required_matches
cropped_series = _crop_to_match_seasons(
detrended_series, required_matches=curr_required_matches)
# perform dft
self.fft_values = np.fft.fft(cropped_series.univariate_values())
# get indices of `nr_freqs_to_keep` (if a correct value was provied) frequencies with the highest amplitudes
# by partitioning around the element with sorted index -nr_freqs_to_keep instead of sorting the whole array
first_n = self.nr_freqs_to_keep
if first_n is None or first_n < 1 or first_n > len(self.fft_values):
first_n = len(self.fft_values)
self.filtered_indices = np.argpartition(abs(self.fft_values),
-first_n)[-first_n:]
# set all other values in the frequency domain to 0
self.fft_values_filtered = np.zeros(len(self.fft_values),
dtype=np.complex_)
self.fft_values_filtered[self.filtered_indices] = self.fft_values[
self.filtered_indices]
# precompute all possible predicted values using inverse dft
self.predicted_values = np.fft.ifft(self.fft_values_filtered).real
return self
def test_multivariate_fill(self):
seriesA: TimeSeries = TimeSeries.from_times_and_values(
self.time,
np.array([np.nan] * 5 + [2.0] * 5 + [np.nan] * 5 + [2.0] * 10 +
[np.nan] * 5),
)
seriesB: TimeSeries = TimeSeries.from_times_and_values(
self.time,
np.array(self.lin[:10] + [np.nan] * 10 + self.lin[-10:]))
self.assertEqual(
self.series1.stack(self.series2),
fill_missing_values(seriesA.stack(seriesB), "auto"),
)
def ts_transform(series: TimeSeries, fill: Union[str, float],
**kwargs) -> TimeSeries:
return fill_missing_values(series, fill, **kwargs)
def test_ffil(self):
seriesD: TimeSeries = TimeSeries.from_times_and_values(
self.time, np.array(self.lin[:20] + [np.nan] * 10))
self.assertEqual(self.series4, fill_missing_values(seriesD, "auto"))
def test_bfill(self):
seriesC: TimeSeries = TimeSeries.from_times_and_values(
self.time, np.array([np.nan] * 10 + self.lin[-20:]))
# Check that auto-backfill works properly
self.assertEqual(self.series3, fill_missing_values(seriesC, "auto"))
def transform(self, data: TimeSeries, **interpolate_kwargs) -> TimeSeries:
super().transform(data)
return fill_missing_values(data, self._fill, **interpolate_kwargs)
