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Fix: FlowState h=1 crash due to over-squeeze in _predict_batch #277

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Fix: FlowState h=1 crash due to over-squeeze in _predict_batch #277

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7a2aa99
tested h=1 on flowstate
Kushagra7777 Nov 20, 2025
78ff376
fix flowstate h=1 crash
Kushagra7777 Nov 26, 2025
ebd6de4
update gitignore
Kushagra7777 Dec 10, 2025
2a2ab44
Merge branch 'main' into issue_242
Kushagra7777 Dec 11, 2025
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1 change: 1 addition & 0 deletions .gitignore
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@AzulGarza AzulGarza Dec 15, 2025

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nice

Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
myenv
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
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Binary file added Brent_v3.M.xlsx
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@AzulGarza AzulGarza Dec 15, 2025

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it seems we are not using this file. it would be best if we remove it.

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291 changes: 286 additions & 5 deletions docs/examples/forecaster-quickstart.ipynb
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Expand Up @@ -247,15 +247,296 @@
]
},
{
"cell_type": "markdown",
"id": "6a8ae523",
"cell_type": "code",
"execution_count": 1,
"id": "9d08da3e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" See https://github.com/google-research/timesfm/blob/master/README.md for updated APIs.\n",
"h = 2 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:p-29740:t-25856:modeling_flowstate.py:__init__:Number of encoder parameters: 7885.8240000000005k\n",
"INFO:p-29740:t-25856:modeling_flowstate.py:__init__:Number of dencoder parameters: 1181.952k (14.99%)\n",
"100%|██████████| 1/1 [00:00<00:00, 1.74it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" unique_id ds FlowState\n",
"0 brent 2025-09-14 -10.250992\n",
"1 brent 2025-09-21 -7.985748\n",
" h = 1 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:p-29740:t-25856:modeling_flowstate.py:__init__:Number of encoder parameters: 7885.8240000000005k\n",
"INFO:p-29740:t-25856:modeling_flowstate.py:__init__:Number of dencoder parameters: 1181.952k (14.99%)\n",
"100%|██████████| 1/1 [00:00<00:00, 2.07it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" unique_id ds FlowState\n",
"0 brent 2025-09-14 -10.250992\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"import pandas as pd\n",
"from timecopilot import TimeCopilotForecaster\n",
"from timecopilot.models.foundation.flowstate import FlowState\n",
"\n",
"\n",
"\n",
"df = pd.read_excel(\n",
" # \"https://timecopilot.s3.amazonaws.com/public/data/events_pageviews.csv\",\n",
" 'D:/AO/timecopilot/Brent_v3.M.xlsx',\n",
" parse_dates=[\"ds\"],\n",
")\n",
"\n",
"df_w = (\n",
" df\n",
" .set_index(\"ds\")\n",
" .groupby(\"unique_id\")[\"y\"]\n",
" .resample(\"W\")\n",
" .sum()\n",
" .reset_index()\n",
")\n",
"\n",
"\n",
"\n",
"tcf = TimeCopilotForecaster(\n",
" models=[\n",
" FlowState(), \n",
" ]\n",
")\n",
"\n",
"print(\"h = 2 \")\n",
"fcst_h2 = tcf.forecast(\n",
" df=df_w,\n",
" h=2,\n",
" freq=\"W\",\n",
")\n",
"print(fcst_h2.head())\n",
"\n",
"print(\" h = 1 \")\n",
"fcst_h1 = tcf.forecast(\n",
" df=df_w,\n",
" h=1,\n",
" freq=\"W\",\n",
")\n",
"print(fcst_h1.head())\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "37ba8935",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" unique_id ds y\n",
"0 brent 2005-09-01 63.48\n",
"1 brent 2005-10-01 58.10\n",
"2 brent 2005-11-01 55.05\n",
"3 brent 2005-12-01 58.98\n",
"4 brent 2006-01-01 65.99\n",
".. ... ... ...\n",
"236 brent 2025-05-01 63.90\n",
"237 brent 2025-06-01 67.61\n",
"238 brent 2025-07-01 72.53\n",
"239 brent 2025-08-01 68.12\n",
"240 brent 2025-09-01 66.75\n",
"\n",
"[241 rows x 3 columns]\n",
" unique_id ds y\n",
"0 Oktoberfest 2020-01-31 25376\n",
"1 Oktoberfest 2020-02-29 28470\n",
"2 Oktoberfest 2020-03-31 23816\n",
"3 Oktoberfest 2020-04-30 46186\n",
"4 Oktoberfest 2020-05-31 31213\n",
".. ... ... ...\n",
"403 Thanksgiving 2025-04-30 47655\n",
"404 Thanksgiving 2025-05-31 48667\n",
"405 Thanksgiving 2025-06-30 56856\n",
"406 Thanksgiving 2025-07-31 38935\n",
"407 Thanksgiving 2025-08-31 50620\n",
"\n",
"[408 rows x 3 columns]\n"
]
}
],
"source": [
"import pandas as pd\n",
"df = pd.read_excel('D:/AO/timecopilot/Brent_v3.M.xlsx')\n",
"print(df)\n",
"\n",
"\n",
"import pandas as pd\n",
"df = pd.read_csv('https://timecopilot.s3.amazonaws.com/public/data/events_pageviews.csv')\n",
"print(df)\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "0fb3f02c",
"metadata": {},
"source": []
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>unique_id</th>\n",
" <th>ds</th>\n",
" <th>y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Oktoberfest</td>\n",
" <td>2020-01-31</td>\n",
" <td>25376</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Oktoberfest</td>\n",
" <td>2020-02-29</td>\n",
" <td>28470</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Oktoberfest</td>\n",
" <td>2020-03-31</td>\n",
" <td>23816</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Oktoberfest</td>\n",
" <td>2020-04-30</td>\n",
" <td>46186</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Oktoberfest</td>\n",
" <td>2020-05-31</td>\n",
" <td>31213</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>403</th>\n",
" <td>Thanksgiving</td>\n",
" <td>2025-04-30</td>\n",
" <td>47655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>404</th>\n",
" <td>Thanksgiving</td>\n",
" <td>2025-05-31</td>\n",
" <td>48667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>405</th>\n",
" <td>Thanksgiving</td>\n",
" <td>2025-06-30</td>\n",
" <td>56856</td>\n",
" </tr>\n",
" <tr>\n",
" <th>406</th>\n",
" <td>Thanksgiving</td>\n",
" <td>2025-07-31</td>\n",
" <td>38935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>407</th>\n",
" <td>Thanksgiving</td>\n",
" <td>2025-08-31</td>\n",
" <td>50620</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>408 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" unique_id ds y\n",
"0 Oktoberfest 2020-01-31 25376\n",
"1 Oktoberfest 2020-02-29 28470\n",
"2 Oktoberfest 2020-03-31 23816\n",
"3 Oktoberfest 2020-04-30 46186\n",
"4 Oktoberfest 2020-05-31 31213\n",
".. ... ... ...\n",
"403 Thanksgiving 2025-04-30 47655\n",
"404 Thanksgiving 2025-05-31 48667\n",
"405 Thanksgiving 2025-06-30 56856\n",
"406 Thanksgiving 2025-07-31 38935\n",
"407 Thanksgiving 2025-08-31 50620\n",
"\n",
"[408 rows x 3 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"display_name": "myenv",
"language": "python",
"name": "python3"
},
Expand All @@ -269,7 +550,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.12"
"version": "3.10.11"
}
},
"nbformat": 4,
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3 changes: 2 additions & 1 deletion timecopilot/models/foundation/flowstate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -167,7 +167,8 @@ def _predict_batch(
batch_first=False,
).prediction_outputs
fcst = fcst.squeeze(-1).transpose(-1, -2) # now shape is (batch, h, quantiles)
fcst_mean = fcst[..., supported_quantiles.index(0.5)].squeeze()
# fcst_mean = fcst[..., supported_quantiles.index(0.5)].squeeze()
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@AzulGarza AzulGarza Dec 15, 2025

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let's remove this commented line.

fcst_mean = fcst[..., supported_quantiles.index(0.5)]
fcst_mean_np = fcst_mean.detach().numpy()
fcst_quantiles_np = fcst.detach().numpy() if quantiles is not None else None
return fcst_mean_np, fcst_quantiles_np
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