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

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]: