reconnect moved files to git repo

venv/lib/python3.11/site-packages/statsmodels/sandbox/archive/tsa.py

@@ -0,0 +1,49 @@
+'''Collection of alternative implementations for time series analysis
+
+
+>>> signal.fftconvolve(x,x[::-1])[len(x)-1:len(x)+10]/x.shape[0]
+array([  2.12286549e+00,   1.27450889e+00,   7.86898619e-02,
+        -5.80017553e-01,  -5.74814915e-01,  -2.28006995e-01,
+         9.39554926e-02,   2.00610244e-01,   1.32239575e-01,
+         1.24504352e-03,  -8.81846018e-02])
+>>> sm.tsa.stattools.acovf(X, fft=True)[:order+1]
+array([  2.12286549e+00,   1.27450889e+00,   7.86898619e-02,
+        -5.80017553e-01,  -5.74814915e-01,  -2.28006995e-01,
+         9.39554926e-02,   2.00610244e-01,   1.32239575e-01,
+         1.24504352e-03,  -8.81846018e-02])
+
+>>> import nitime.utils as ut
+>>> ut.autocov(s)[:order+1]
+array([  2.12286549e+00,   1.27450889e+00,   7.86898619e-02,
+        -5.80017553e-01,  -5.74814915e-01,  -2.28006995e-01,
+         9.39554926e-02,   2.00610244e-01,   1.32239575e-01,
+         1.24504352e-03,  -8.81846018e-02])
+'''
+import numpy as np
+
+
+def acovf_fft(x, demean=True):
+    '''autocovariance function with call to fftconvolve, biased
+
+    Parameters
+    ----------
+    x : array_like
+        timeseries, signal
+    demean : bool
+        If true, then demean time series
+
+    Returns
+    -------
+    acovf : ndarray
+        autocovariance for data, same length as x
+
+    might work for nd in parallel with time along axis 0
+
+    '''
+    from scipy import signal
+    x = np.asarray(x)
+
+    if demean:
+        x = x - x.mean()
+
+    signal.fftconvolve(x,x[::-1])[len(x)-1:len(x)+10]/x.shape[0]