Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

[BUG] fix nlag logic in SeasonalityACF and SeasonalityACFqstat #4171

Merged
merged 1 commit into from Feb 4, 2023
Merged

[BUG] fix nlag logic in SeasonalityACF and SeasonalityACFqstat #4171

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
20 changes: 11 additions & 9 deletions sktime/param_est/seasonality.py
View file
Open in desktop
Expand Up @@ -27,15 +27,16 @@ class SeasonalityACF(BaseParamFitter):
Parameters
----------
candidate_sp : None, int or list of int, optional, default = None
candidate sp to test, and to restrict tests to
if None, will test all integer lags between 1 and nlags
candidate sp to test, and to restrict tests to; ints must be 2 or larger
if None, will test all integer lags between 2 and `nlags` (inclusive)
p_threshold : float, optional, default=0.05
significance threshold to apply in tesing for seasonality
adjusted : bool, optional, default=False
If True, then denominators for autocovariance are n-k, otherwise n.
nlags : int, optional, default=None
Number of lags to compute autocorrelations for and select from.
At default None, uses min(10 * np.log10(nobs), nobs - 1).
At default None, uses `min(10 * np.log10(nobs), nobs - 1)`.
Will be ignored if `candidate_sp` is provided.
fft : bool, optional, default=True
If True, computes the ACF via FFT.
missing : str, ["none", "raise", "conservative", "drop"], optional, default="none"
Expand Down Expand Up @@ -140,7 +141,7 @@ def _fit(self, X):

candidate_sp = self.candidate_sp
if candidate_sp is None:
candidate_sp = range(2, nlags)
candidate_sp = range(2, nlags + 1)

fft = self.fft
missing = self.missing
Expand Down Expand Up @@ -220,8 +221,8 @@ class SeasonalityACFqstat(BaseParamFitter):
Parameters
----------
candidate_sp : None, int or list of int, optional, default = None
candidate sp to test, and to restrict tests to
if None, will test all integer lags between 1 and nlags
candidate sp to test, and to restrict tests to; ints must be 2 or larger
if None, will test all integer lags between 2 and `nlags` (inclusive)
p_threshold : float, optional, default=0.05
significance threshold to apply in tesing for seasonality
p_adjust : str, optional, default="fdr_by" (Benjamini/Yekutieli)
Expand All @@ -235,7 +236,8 @@ class SeasonalityACFqstat(BaseParamFitter):
If True, then denominators for autocovariance are n-k, otherwise n.
nlags : int, optional, default=None
Number of lags to compute autocorrelations for and select from.
At default None, uses min(10 * np.log10(nobs), nobs - 1).
At default None, uses `min(10 * np.log10(nobs), nobs - 1)`.
Will be ignored if `candidate_sp` is provided.
fft : bool, optional, default=True
If True, computes the ACF via FFT.
missing : str, ["none", "raise", "conservative", "drop"], optional, default="none"
Expand Down Expand Up @@ -329,7 +331,7 @@ def _fit(self, X):

candidate_sp = self.candidate_sp
if candidate_sp is None:
candidate_sp = range(2, nlags)
candidate_sp = range(2, nlags + 1)

fft = self.fft
missing = self.missing
Expand All @@ -354,7 +356,7 @@ def _fit(self, X):
else:
qstat_cand = qstat
pvalues_cand = pvalues
candidate_sp = range(2, nlags)
candidate_sp = range(2, nlags + 1)

self.qstat_cand_ = qstat_cand
self.pvalues_cand = pvalues_cand
Expand Down