Max (Nixtla)
03/02/2022, 3:25 PMMarco Gorelli
03/07/2022, 1:53 PMindex=pd.Index([0] * ap_test.size, name='unique_id')
It is something like that 0
indicates points belonging to one time series, 1
would indicate points belonging to another time series, and that by putting them all into the same dataframe and running auto_arima
once it’s more efficient than to run auto_arima
separately for each time series?Matthew Baron
03/11/2022, 3:59 PMMatthew Baron
03/11/2022, 8:29 PMStatsForecast().forecast(duration)
, it would be handy to be able to pass in the specific values of ds
that I want to predict for.
for instance, I might have trained a weekly forecaster, but want a prediction mid-week.Max (Nixtla)
03/14/2022, 2:15 PMfede (nixtla) (they/them)
03/14/2022, 6:23 PMJuan David Ferreira
03/15/2022, 11:38 PMnbdev
.
I study the documentation and I was also able to analyze the neuralforecast
repository.
Some things I could understand studying but others at the moment I don't understand. What interests me is:
- how to test using nbdev?
- How to put into production the library developed with nbdev?. (both in Conda and in PyPI).
If you have resources to share, I would appreciate it.
PS: later I will start studying more about forecast
. Thank you.Max (Nixtla)
03/26/2022, 12:55 AMMax (Nixtla)
03/29/2022, 2:17 PMMax (Nixtla)
04/01/2022, 8:00 PMsky Li
04/01/2022, 8:01 PMsky Li
04/01/2022, 8:08 PMsky Li
04/01/2022, 8:10 PMKin Gtz. Olivares
04/01/2022, 8:23 PMPatricio
04/04/2022, 7:56 PMErik Bovee
04/04/2022, 8:02 PMrariwa
04/09/2022, 11:17 PMAssertionError Traceback (most recent call last)
Input In [32], in <cell line: 61>()
57 mc['n_layers'] = len(mc['stack_types']) * [ mc['constant_n_layers'] ]
59 from neuralforecast.experiments.utils import create_datasets
---> 61 train_dataset, val_dataset, test_dataset, scaler_y = create_datasets(mc=mc,
62 S_df=s_df, Y_df=y_df, X_df=x_df,
63 f_cols=['Exogenous1', 'Exogenous2'],
64 ds_in_val=180,
65 ds_in_test=984)
67 train_loader = TimeSeriesLoader(dataset=train_dataset,
68 batch_size=int(mc['batch_size']),
69 n_windows=mc['n_windows'],
70 shuffle=True)
72 val_loader = TimeSeriesLoader(dataset=val_dataset,
73 batch_size=int(mc['batch_size']),
74 shuffle=False)
File ~\Anaconda3\envs\SiT\lib\site-packages\neuralforecast\experiments\utils.py:249, in create_datasets(mc, S_df, Y_df, X_df, f_cols, ds_in_test, ds_in_val, verbose)
244 train_mask_df, valid_mask_df, test_mask_df = get_mask_dfs(Y_df=Y_df,
245 ds_in_val=ds_in_val,
246 ds_in_test=ds_in_test)
248 #---------------------------------------------- Scale Data ----------------------------------------------#
--> 249 Y_df, X_df, scaler_y = scale_data(Y_df=Y_df, X_df=X_df, mask_df=train_mask_df,
250 normalizer_y=mc['normalizer_y'], normalizer_x=mc['normalizer_x'])
252 #----------------------------------------- Declare Dataset and Loaders ----------------------------------#
254 if mc['mode'] == 'simple':
File ~\Anaconda3\envs\SiT\lib\site-packages\neuralforecast\experiments\utils.py:202, in scale_data(Y_df, X_df, mask_df, normalizer_y, normalizer_x)
200 for col in X_cols:
201 scaler_x = Scaler(normalizer=normalizer_x)
--> 202 X_df[col] = scaler_x.scale(x=X_df[col].values, mask=mask)
204 return Y_df, X_df, scaler_y
File ~\Anaconda3\envs\SiT\lib\site-packages\neuralforecast\data\scalers.py:43, in Scaler.scale(self, x, mask)
40 elif self.normalizer == 'norm1':
41 x_scaled, x_shift, x_scale = norm1_scaler(x, mask)
---> 43 assert len(x[mask==1] == np.sum(mask)), 'Something weird is happening, call Cristian'
44 nan_before_scale = np.sum(np.isnan(x))
45 nan_after_scale = np.sum(np.isnan(x_scaled))
AssertionError: Something weird is happening, call Cristian
here is my datarariwa
04/09/2022, 11:20 PMMário Amorim Lopes
04/10/2022, 11:26 AMMax (Nixtla)
04/12/2022, 5:49 PMMark Raphael
04/12/2022, 5:54 PMMax (Nixtla)
04/13/2022, 12:52 AMMax (Nixtla)
04/13/2022, 3:25 AMMax (Nixtla)
04/28/2022, 3:06 PMGrady Matthias Oktavian
05/02/2022, 9:00 AMnixtla
statsforecastGrady Matthias Oktavian
05/02/2022, 9:01 AMmlforecast
and wished to know, can I train several time series at once using 1 LGBM Regressor model? I believe this forecasting technique is called 'global forecasting' in which multiple univariate time series are used as training data for 1 ML model.Grady Matthias Oktavian
05/02/2022, 9:01 AMmlforecast
? Thank you!Mário Amorim Lopes
05/02/2022, 12:32 PMMax (Nixtla)
05/02/2022, 5:27 PMMax (Nixtla)
05/03/2022, 12:26 AM