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Cross validation mods clean #329

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ecb840e
A cool tutorial
elmartinj Dec 2, 2025
1ae25dc
Change merging method
elmartinj Dec 2, 2025
ee9172a
run pre-comit hooks
Dec 10, 2025
f380bcb
Changing filename to requested formats and folder. This also contains…
Dec 11, 2025
af1d305
Clearance of verbose cells
Dec 11, 2025
2c14221
Staging changes to mkdocs and the naming of the tutorial file
Dec 15, 2025
bbef342
adding changes to the agent dataframe processing in order to reduce t…
elmartinj Mar 9, 2026
0a25377
Adding first approach and modifications to the agent and forecaster s…
elmartinj Mar 13, 2026
97b28fb
adding final revisions to the scripts in order to allow different for…
elmartinj Mar 13, 2026
6e6a541
pre-commit corrections in order to sort out formatting
elmartinj Mar 13, 2026
d33336d
Merge branch 'main' into cross_validation_mods_clean
elmartinj Mar 19, 2026
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85 changes: 39 additions & 46 deletions docs/examples/cryptocurrency-quickstart.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -63,7 +63,7 @@
"outputs": [],
"source": [
"files = os.listdir(path)\n",
"files = [path + \"/\" + x for x in files]"
"files = [path+'/'+x for x in files]"
]
},
{
Expand Down Expand Up @@ -198,18 +198,18 @@
"# Read all filez and set them up to the readable structure for timecopilot\n",
"for file in files:\n",
" temp_df = pd.read_csv(file)\n",
" temp_df = temp_df[[\"Symbol\", \"Date\", \"Close\"]]\n",
" temp_df.columns = [\"unique_id\", \"ds\", \"y\"]\n",
" big_df = pd.concat([big_df, temp_df])\n",
" temp_df = temp_df[['Symbol','Date','Close']]\n",
" temp_df.columns = ['unique_id','ds','y']\n",
" big_df = pd.concat([big_df,temp_df])\n",
"\n",
"big_df = big_df.reset_index(drop=True)\n",
"big_df[\"ds\"] = pd.to_datetime(big_df[\"ds\"], dayfirst=True, errors=\"coerce\")\n",
"\n",
"# This line will be kept for execution time sanity, feel free to remove it if you want to stress timing a little further.\n",
"# This line will be kept for execution time sanity, feel free to remove it if you want to stress timing a little further. \n",
"# big_df = big_df[big_df.ds >= \"2021-01-01\"]\n",
"cryptos = [\"MIOTA\", \"XEM\", \"ETH\", \"LTC\", \"DOGE\", \"CRO\", \"USDC\", \"ADA\"]\n",
"big_df = big_df[big_df.unique_id.isin(cryptos)]\n",
"big_df = big_df.reset_index(drop=True)\n",
"cryptos=['MIOTA','XEM','ETH','LTC','DOGE','CRO','USDC','ADA']\n",
"big_df=big_df[big_df.unique_id.isin(cryptos)]\n",
"big_df=big_df.reset_index(drop=True)\n",
"big_df"
]
},
Expand Down Expand Up @@ -341,7 +341,6 @@
" df_out.loc[idx, col] = np.nan\n",
" return df_out\n",
"\n",
"\n",
"df_missing = add_missing(big_df, col=\"y\", frac=0.03, seed=42)\n",
"df_missing = df_missing.sample(frac=1, random_state=42).reset_index(drop=True)\n",
"print(df_missing)"
Expand Down Expand Up @@ -710,14 +709,12 @@
}
],
"source": [
"anomaly_summary_xlm = anomalies_df[\n",
"anomaly_summary_xlm=anomalies_df[\n",
" # (anomalies_df.unique_id=='SOL') & \\\n",
" (\n",
" (anomalies_df[\"Chronos-anomaly\"] == True)\n",
" | (anomalies_df[\"SeasonalNaive-anomaly\"] == True)\n",
" | (anomalies_df[\"Theta-anomaly\"] == True)\n",
" )\n",
"].reset_index(drop=True)\n",
" ((anomalies_df['Chronos-anomaly']==True) | \\\n",
" (anomalies_df['SeasonalNaive-anomaly']==True) |\n",
" (anomalies_df['Theta-anomaly']==True)\n",
" )].reset_index(drop=True)\n",
"anomaly_summary_xlm"
]
},
Expand Down Expand Up @@ -957,14 +954,12 @@
}
],
"source": [
"anomaly_summary_xlm = anomalies_df[\n",
" (anomalies_df.unique_id == \"ADA\")\n",
" & (\n",
" (anomalies_df[\"Chronos-anomaly\"] == True)\n",
" | (anomalies_df[\"SeasonalNaive-anomaly\"] == True)\n",
" | (anomalies_df[\"Theta-anomaly\"] == True)\n",
" )\n",
"].reset_index(drop=True)\n",
"anomaly_summary_xlm=anomalies_df[\n",
" (anomalies_df.unique_id=='ADA') & \\\n",
" ((anomalies_df['Chronos-anomaly']==True) | \\\n",
" (anomalies_df['SeasonalNaive-anomaly']==True) |\n",
" (anomalies_df['Theta-anomaly']==True)\n",
" )].reset_index(drop=True)\n",
"anomaly_summary_xlm"
]
},
Expand Down Expand Up @@ -1204,14 +1199,12 @@
}
],
"source": [
"anomaly_summary_xlm = anomalies_df[\n",
" (anomalies_df.unique_id == \"ADA\")\n",
" & (\n",
" (anomalies_df[\"Chronos-anomaly\"] == True)\n",
" & (anomalies_df[\"SeasonalNaive-anomaly\"] == True)\n",
" # (anomalies_df['Theta-anomaly']==True)\n",
" )\n",
"].reset_index(drop=True)\n",
"anomaly_summary_xlm=anomalies_df[\n",
" (anomalies_df.unique_id=='ADA') & \\\n",
" ((anomalies_df['Chronos-anomaly']==True) & \\\n",
" (anomalies_df['SeasonalNaive-anomaly']==True) \\\n",
" # (anomalies_df['Theta-anomaly']==True)\n",
" )].reset_index(drop=True)\n",
"anomaly_summary_xlm"
]
},
Expand Down Expand Up @@ -1248,12 +1241,12 @@
"source": [
"tcf1 = TimeCopilotForecaster(\n",
" models=[\n",
" AutoARIMA(),\n",
" AutoARIMA(), \n",
" Chronos(repo_id=\"amazon/chronos-bolt-mini\"),\n",
" Theta(),\n",
" AutoETS(),\n",
" Moirai(),\n",
" Prophet(),\n",
" AutoETS(), \n",
" Moirai(), \n",
" Prophet(), \n",
" SeasonalNaive(),\n",
" ]\n",
")"
Expand All @@ -1266,7 +1259,7 @@
"metadata": {},
"outputs": [],
"source": [
"fcst_df = tcf1.forecast(df=big_df, h=30, level=[80, 90])"
"fcst_df = tcf1.forecast(df=big_df, h=30, level=[80,90])"
]
},
{
Expand Down Expand Up @@ -1310,9 +1303,9 @@
"metadata": {},
"outputs": [],
"source": [
"eth_fcst_normal = fcst_df[(fcst_df.unique_id == \"ETH\")][\n",
" [\"unique_id\", \"ds\", \"Chronos\", \"Chronos-lo-80\"]\n",
"].reset_index(drop=True)"
"eth_fcst_normal=fcst_df[(fcst_df.unique_id=='ETH')]\\\n",
" [['unique_id','ds','Chronos','Chronos-lo-80']]\\\n",
" .reset_index(drop=True)"
]
},
{
Expand Down Expand Up @@ -1352,9 +1345,9 @@
"metadata": {},
"outputs": [],
"source": [
"eth_fcst_missing = fcst_df[(fcst_df.unique_id == \"ETH\")][\n",
" [\"unique_id\", \"ds\", \"Chronos\", \"Chronos-lo-80\"]\n",
"].reset_index(drop=True)"
"eth_fcst_missing=fcst_df[(fcst_df.unique_id=='ETH')]\\\n",
" [['unique_id','ds','Chronos','Chronos-lo-80']]\\\n",
" .reset_index(drop=True)"
]
},
{
Expand Down Expand Up @@ -1522,9 +1515,9 @@
}
],
"source": [
"compare = eth_fcst_normal.merge(eth_fcst_missing, on=[\"ds\", \"unique_id\"])\n",
"compare[\"dif\"] = abs(compare[\"Chronos_x\"] - compare[\"Chronos_y\"])\n",
"print(compare[\"dif\"].sum())"
"compare=eth_fcst_normal.merge(eth_fcst_missing,on=['ds','unique_id'])\n",
"compare['dif']=abs(compare['Chronos_x']-compare['Chronos_y'])\n",
"print(compare['dif'].sum())"
]
},
{
Expand Down
4 changes: 1 addition & 3 deletions mkdocs.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -22,12 +22,12 @@ nav:
- examples/agent-quickstart.ipynb
- examples/llm-providers.ipynb
- examples/aws-bedrock.ipynb
- examples/google-llms.ipynb
- examples/forecaster-quickstart.ipynb
- examples/anomaly-detection-forecaster-quickstart.ipynb
- examples/ts-foundation-models-comparison-quickstart.ipynb
- examples/gift-eval.ipynb
- examples/chronos-family.ipynb
- examples/crytpocurrency-quickstart.ipynb
- examples/finetuning.ipynb
- examples/cryptocurrency-quickstart.ipynb
- examples/sktime.ipynb
Expand All @@ -47,7 +47,6 @@ nav:
- api/models/ml.md
- api/models/neural.md
- api/models/ensembles.md
- api/models/adapters/adapters.md
- api/models/utils/forecaster.md
- api/gift-eval/gift-eval.md
- Changelogs:
Expand Down Expand Up @@ -76,7 +75,6 @@ nav:

theme:
name: "material"
custom_dir: docs/overrides
logo: https://timecopilot.s3.amazonaws.com/public/logos/logo-white.svg
favicon: https://timecopilot.s3.amazonaws.com/public/logos/favicon-white.svg
palette:
Expand Down
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