def test_pmdarima_diff_inv_fails_with_invalid_data(data):
analyzer = PmdarimaAnalyzer(df=data.df,
group_key_columns=data.key_columns,
y_col="y",
datetime_col="ds")
diff = analyzer.generate_diff(lag=1, differences=1)
with pytest.raises(
DivinerException,
match="group_diff_data does not contain the key `diff`"):
diff_mod = {}
for key, value in diff.items():
diff_mod[key] = {"series_start": value.get("series_start")}
analyzer.generate_diff_inversion(group_diff_data=diff_mod,
lag=1,
differences=1,
recenter=True)
with pytest.warns(
UserWarning,
match="Recentering is not possible due to `series_start` missing"):
diff_mod = {}
for key, value in diff.items():
diff_mod[key] = {"diff": value.get("diff")}
analyzer.generate_diff_inversion(group_diff_data=diff_mod,
lag=1,
differences=1,
recenter=True)
def test_pmdarima_reconstruct_series_from_diff_inv(data):
analyzer = PmdarimaAnalyzer(df=data.df,
group_key_columns=data.key_columns,
y_col="y",
datetime_col="ds")
diff = analyzer.generate_diff(lag=2, differences=1)
group_dfs = analyzer._group_df
inverted = analyzer.generate_diff_inversion(diff,
lag=2,
differences=1,
recenter=True)
for group, data in group_dfs:
assert_allclose(data["y"], inverted.get(group), rtol=0.1)
_print_dict(nsdiffs, "Seasonal Differencing")
# Get the autocorrelation function for each group
group_acf = analyzer.calculate_acf(unbiased=True,
nlags=120,
qstat=True,
fft=True,
alpha=0.05,
adjusted=True)
_print_dict(group_acf, "Autocorrelation function")
# Get the partial autocorrelation function for each group
group_pacf = analyzer.calculate_pacf(nlags=120, method="yw", alpha=0.05)
_print_dict(group_pacf, "Partial Autocorrelation function")
# Perform a diff operation on each group
group_diff = analyzer.generate_diff(lag=7, differences=1)
_print_dict(group_diff, "Differencing")
# Invert the diff operation on each group
group_diff_inv = analyzer.generate_diff_inversion(group_diff,
lag=7,
differences=1,
recenter=True)
_print_dict(group_diff_inv, "Differencing Inversion")