re-calculate alpha each iteration for irregular-spaced time series

Author: tserrao

pandas/_libs/window/aggregations.pyx

@@ -1813,6 +1813,9 @@ def ewm(const float64_t[:] vals, const int64_t[:] start, const int64_t[:] end,
                             if normalize:
                                 # avoid numerical errors on constant series
                                 if weighted != cur:
+                                    if not adjust and com == 1:
+                                        # update alpha "on the fly" for irregular-interval time series
+                                        new_wt = 1. - old_wt
                                     weighted = old_wt * weighted + new_wt * cur
                                     weighted /= (old_wt + new_wt)
                                 if adjust:

pandas/core/window/numba_.py

@@ -149,6 +149,9 @@ def ewm(
                             # note that len(deltas) = len(vals) - 1 and deltas[i]
                             # is to be used in conjunction with vals[i+1]
                             old_wt *= old_wt_factor ** deltas[start + j - 1]
+                            if not adjust and com == 1:
+                                # update alpha "on the fly" for irregular-interval time series
+                                new_wt = 1. - old_wt
                         else:
                             weighted = old_wt_factor * weighted
                         if is_observation:
@@ -324,6 +327,9 @@ def ewm_table(
                             # note that len(deltas) = len(vals) - 1 and deltas[i]
                             # is to be used in conjunction with vals[i+1]
                             old_wt[j] *= old_wt_factor ** deltas[i - 1]
+                            if not adjust and com == 1:
+                                # update alpha "on the fly" for irregular-interval time series
+                                new_wt = 1. - old_wt[j]
                         else:
                             weighted[j] = old_wt_factor * weighted[j]
                         if is_observations[j]: