Closed LeonTing1010 closed 5 months ago
elephaint
commented
5 months ago Hi,
I can't reproduce the issue from this piece of code - from the error it seems that there is something in your input data that is not of the correct datatype.
Could you provide a fully, standalone, piece of code so I can reproduce?
LeonTing1010
commented
5 months ago Hi,
I can't reproduce the issue from this piece of code - from the error it seems that there is something in your input data that is not of the correct datatype.
Could you provide a fully, standalone, piece of code so I can reproduce?
The issue has been resolved. It was because my dates were in string format.
What happened + What you expected to happen
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ /Users/leo/web3/LLM/langchain/mlts/test.py:30 in │
│ │
│ 27 nf = NeuralForecast(models=models, freq="D") │
│ 28 │
│ 29 nf.fit(df=Y_train_df) │
│ ❱ 30 Y_hat_df = nf.predict().reset_index() │
│ 31 │
│ 32 # Plot predictions │
│ 33 fig, ax = plt.subplots(1, 1, figsize=(20, 7)) │
│ │
│ /Users/leo/web3/LLM/langchain/venv/lib/python3.10/site-packages/neuralforecast/core.py:304 in │
│ predict │
│ │
│ 301 │ │ │ cols += [model_name + n for n in model.loss.output_names] │
│ 302 │ │ │
│ 303 │ │ # Placeholder dataframe for predictions with unique_id and ds │
│ ❱ 304 │ │ fcsts_df = _future_dates( │
│ 305 │ │ │ dataset=dataset, uids=uids, last_dates=last_dates, freq=self.freq, h=self.h │
│ 306 │ │ ) │
│ 307 │
│ │
│ /Users/leo/web3/LLM/langchain/venv/lib/python3.10/site-packages/neuralforecast/core.py:107 in │
│ _future_dates │
│ │
│ 104 │ else: │
│ 105 │ │ last_date_f = lambda x: pd.date_range(x + freq, periods=h, freq=freq) │
│ 106 │ if len(np.unique(last_dates)) == 1: │
│ ❱ 107 │ │ dates = np.tile(last_date_f(last_dates[0]), len(dataset)) │
│ 108 │ else: │
│ 109 │ │ dates = np.hstack([last_date_f(last_date) for last_date in last_dates]) │
│ 110 │ idx = pd.Index(np.repeat(uids, h), name="unique_id") │
│ │
│ /Users/leo/web3/LLM/langchain/venv/lib/python3.10/site-packages/neuralforecast/core.py:105 in │
│ │
│ │
│ 102 │ if issubclass(last_dates.dtype.type, np.integer): │
│ 103 │ │ last_date_f = lambda x: np.arange(x + 1, x + 1 + h, dtype=last_dates.dtype) │
│ 104 │ else: │
│ ❱ 105 │ │ last_date_f = lambda x: pd.date_range(x + freq, periods=h, freq=freq) │
│ 106 │ if len(np.unique(last_dates)) == 1: │
│ 107 │ │ dates = np.tile(last_date_f(last_dates[0]), len(dataset)) │
│ 108 │ else: │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
TypeError: can only concatenate str (not "pandas._libs.tslibs.offsets.Day") to str
Versions / Dependencies
Name: neuralforecast Version: 1.5.0 Summary: Time series forecasting suite using deep learning models Home-page: https://github.com/Nixtla/neuralforecast/ Author: Nixtla Author-email: business@nixtla.io License: Apache Software License 2.0 Location: /Users/leo/web3/LLM/langchain/venv/lib/python3.10/site-packages Requires: numpy, pandas, pytorch-lightning, ray, torch Required-by:
Reproduction script
Split data and declare panel datasee
Y_train_df = Y_df[Y_df.ds <= '2024-04-03'] # 132 train Y_test_df = Y_df[(Y_df.ds > '2024-04-03') & (Y_df.ds < '2024-04-15')] # 12 test
Fit and predict with NBEATS and NHITS models
horizon = len(Y_test_df) models = [NBEATS(input_size=2 horizon, h=horizon, max_steps=1), NHITS(input_size=2 horizon, h=horizon, max_steps=1)]
nf = NeuralForecast(models=models, freq="D")
nf.fit(df=Y_train_df) Y_hat_df = nf.predict().reset_index()
Plot predictions
fig, ax = plt.subplots(1, 1, figsize=(20, 7)) Y_hat_df = Y_test_df.merge(Y_hat_df, how='left', on=['unique_id', 'ds']) plot_df = pd.concat([Y_train_df, Y_hat_df]).set_index('ds')
plot_df[['y', 'NBEATS', 'NHITS']].plot(ax=ax, linewidth=2)
ax.set_title('AirPassengers Forecast', fontsize=22) ax.set_ylabel('Monthly Passengers', fontsize=20) ax.set_xlabel('Timestamp [t]', fontsize=20) ax.legend(prop={'size': 15}) ax.grid() plt.show()
Issue Severity
None