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fix arima trained models results index #858

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Jul 15, 2024
Merged

fix arima trained models results index #858

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10 changes: 5 additions & 5 deletions nbs/src/arima.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -3057,15 +3057,15 @@
" if fit['ic'] < bestfit['ic']:\n",
" bestfit = fit\n",
" p = q = P = Q = 0\n",
" k = 1\n",
" k = 2\n",
" if max_p > 0 or max_P > 0:\n",
" p_ = int(max_p > 0)\n",
" P_ = int(m > 1 and max_P > 0)\n",
" fit = p_myarima(\n",
" order=(p_, d, 0),\n",
" seasonal={'order': (P_, D, 0), 'period': m},\n",
" )\n",
" results[k + 1] = (p_, d, 0, P_, D, 0, constant, fit['ic'])\n",
" results[k] = (p_, d, 0, P_, D, 0, constant, fit['ic'])\n",
" if fit['ic'] < bestfit['ic']:\n",
" bestfit = fit\n",
" p = p_\n",
Expand All @@ -3079,7 +3079,7 @@
" order=(0, d, q_),\n",
" seasonal={'order': (0, D, Q_), 'period': m},\n",
" )\n",
" results[k + 1] = (0, d, q_, 0, D, Q_, constant, fit['ic'])\n",
" results[k] = (0, d, q_, 0, D, Q_, constant, fit['ic'])\n",
" if fit['ic'] < bestfit['ic']:\n",
" bestfit = fit\n",
" p = P = 0\n",
Expand All @@ -3092,14 +3092,13 @@
" seasonal={'order': (0, D, 0), 'period': m},\n",
" constant=False,\n",
" )\n",
" results[k + 1] = (0, d, 0, 0, D, 0, 0, fit['ic'])\n",
" results[k] = (0, d, 0, 0, D, 0, 0, fit['ic'])\n",
" if fit['ic'] < bestfit['ic']:\n",
" bestfit = fit\n",
" p = q = P = Q = 0\n",
" k += 1\n",
" \n",
" def try_params(p, d, q, P, D, Q, constant, k, bestfit):\n",
" k += 1\n",
" improved = False\n",
" if k >= results.shape[0]:\n",
" return k, bestfit, improved\n",
Expand All @@ -3108,6 +3107,7 @@
" seasonal={'order': (P, D, Q), 'period': m},\n",
" )\n",
" results[k] = (p, d, q, P, D, Q, constant, fit['ic'])\n",
" k += 1\n",
" if fit['ic'] < bestfit['ic']:\n",
" bestfit = fit\n",
" improved = True\n",
Expand Down
10 changes: 5 additions & 5 deletions statsforecast/arima.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2101,15 +2101,15 @@ def auto_arima_f(
if fit["ic"] < bestfit["ic"]:
bestfit = fit
p = q = P = Q = 0
k = 1
k = 2
if max_p > 0 or max_P > 0:
p_ = int(max_p > 0)
P_ = int(m > 1 and max_P > 0)
fit = p_myarima(
order=(p_, d, 0),
seasonal={"order": (P_, D, 0), "period": m},
)
results[k + 1] = (p_, d, 0, P_, D, 0, constant, fit["ic"])
results[k] = (p_, d, 0, P_, D, 0, constant, fit["ic"])
if fit["ic"] < bestfit["ic"]:
bestfit = fit
p = p_
Expand All @@ -2123,7 +2123,7 @@ def auto_arima_f(
order=(0, d, q_),
seasonal={"order": (0, D, Q_), "period": m},
)
results[k + 1] = (0, d, q_, 0, D, Q_, constant, fit["ic"])
results[k] = (0, d, q_, 0, D, Q_, constant, fit["ic"])
if fit["ic"] < bestfit["ic"]:
bestfit = fit
p = P = 0
Expand All @@ -2136,14 +2136,13 @@ def auto_arima_f(
seasonal={"order": (0, D, 0), "period": m},
constant=False,
)
results[k + 1] = (0, d, 0, 0, D, 0, 0, fit["ic"])
results[k] = (0, d, 0, 0, D, 0, 0, fit["ic"])
if fit["ic"] < bestfit["ic"]:
bestfit = fit
p = q = P = Q = 0
k += 1

def try_params(p, d, q, P, D, Q, constant, k, bestfit):
k += 1
improved = False
if k >= results.shape[0]:
return k, bestfit, improved
Expand All @@ -2152,6 +2151,7 @@ def try_params(p, d, q, P, D, Q, constant, k, bestfit):
seasonal={"order": (P, D, Q), "period": m},
)
results[k] = (p, d, q, P, D, Q, constant, fit["ic"])
k += 1
if fit["ic"] < bestfit["ic"]:
bestfit = fit
improved = True
Expand Down
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