Max (Nixtla)
02/21/2023, 8:44 PMMax (Nixtla)
02/21/2023, 8:45 PMValeriy
02/21/2023, 8:46 PMValeriy
02/22/2023, 8:23 AMAndrei Tulbure
02/22/2023, 11:39 AMValeriy
02/22/2023, 12:46 PMValeriy
02/22/2023, 12:48 PMAkmal Soliev
02/22/2023, 3:11 PMdf = process_df()
models = [
AutoARIMA(max_D=12, season_length=12),
]
sf = StatsForecast(
df=df,
sort_df=True,
models=models,
freq='M',
n_jobs=-1,
)
Andrei Tulbure
02/23/2023, 10:26 AMAndrei Tulbure
02/23/2023, 4:17 PMReesu Jagan
02/23/2023, 5:35 PMAndrei Tulbure
02/23/2023, 9:25 PMFrancesco Catanzariti
02/24/2023, 5:37 PMAkmal Soliev
02/24/2023, 8:53 PMenvironment.yml
was created using conda due to the inability to normally install prophet with pip?Piotr Pomorski
02/25/2023, 1:33 PMAndrew Doherty
02/27/2023, 10:13 AMAndrew Doherty
02/27/2023, 10:13 AMAndrew Doherty
02/27/2023, 10:13 AMAndrew Doherty
02/27/2023, 10:15 AMMax (Nixtla)
02/28/2023, 5:47 PMMax (Nixtla)
02/28/2023, 5:53 PMMerlin
03/01/2023, 7:31 AMAmogh Kokari
03/02/2023, 5:38 PMAmogh Kokari
03/02/2023, 7:46 PMNo regressors provided
getting error for forecast.predictArjun
03/03/2023, 12:14 AMValueError Traceback (most recent call last)
Cell In[185], line 3
1 hrec = HierarchicalReconciliation(reconcilers=reconcilers)
----> 3 reconciled_forecasts = hrec.reconcile(Y_hat_df=forecasts_df, S=S, tags=tags2)
File ~\Anaconda3\envs\pycaret\lib\site-packages\hierarchicalforecast\core.py:265, in HierarchicalReconciliation.reconcile(self, Y_hat_df, S, tags, Y_df, level, intervals_method, num_samples, seed, sort_df)
261 fcsts_model = reconciler.predict(S=reconciler_args['S'],
262 y_hat=reconciler_args['y_hat'], level=level)
263 else:
264 # Memory efficient reconciler's fit_predict
--> 265 fcsts_model = reconcile_fn(**kwargs, level=level)
267 # Parse final outputs
268 Y_tilde_df[recmodel_name] = fcsts_model['mean'].flatten()
File ~\Anaconda3\envs\pycaret\lib\site-packages\hierarchicalforecast\methods.py:468, in MiddleOut.fit_predict(self, S, y_hat, tags, y_insample, level, intervals_method)
456 """Middle Out Reconciliation Method.
457
458 **Parameters:**<br>
(...)
465 `y_tilde`: Reconciliated y_hat using the Middle Out approach.
466 """
467 if not is_strictly_hierarchical(S, tags):
--> 468 raise ValueError('Middle out reconciliation requires strictly hierarchical structures.')
469 if self.middle_level not in tags.keys():
470 raise ValueError('You have to provide a `middle_level` in `tags`.')
ValueError: Middle out reconciliation requires strictly hierarchical structures.
This is how my tags looks like :
{'Total': array(['Total'], dtype=object),
'Total/level0': array(['DRAM_DRAM_M', 'NAND_SSD', 'DRAM_DRAM', 'NAND_FLASH'], dtype=object),
'Total/level0/Level1': array(['DRAM_DRAM_M_DDR3', 'DRAM_DRAM_M_DDR4', 'NAND_SSD_PCI-e',
'NAND_SSD_SATA', 'DRAM_DRAM_DDR4', 'DRAM_DRAM_LPDDR4X',
'NAND_FLASH_COMPONENT', 'DRAM_DRAM_HBM', 'DRAM_DRAM_M_DDR5'],
dtype=object),
'Total/level0/Level1/Level2': array(['DRAM_DRAM_M_DDR3_DDR3', 'DRAM_DRAM_M_DDR4_DDR4',
'NAND_SSD_PCI-e_PCI-e', 'NAND_SSD_SATA_SATA',
'DRAM_DRAM_DDR4_DDR4', 'DRAM_DRAM_LPDDR4X_LPDDR4X',
'NAND_FLASH_COMPONENT_COMPONENT', 'DRAM_DRAM_HBM_HBM',
'DRAM_DRAM_M_DDR5_DDR5'], dtype=object)}
can anyone pls help ?Wouter Bles
03/04/2023, 10:20 PMPiotr Pomorski
03/05/2023, 6:32 PMmlf = MLForecast(models=models, differences=[1], date_features=['month'],
lags=None, num_threads=mp.cpu_count()-1,
lag_transforms=None, freq='W-FRI')
Where it just transforms the price series via differences=[1]
, I always end up with ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
Well, I guess it's obvious that the moment you apply first-difference there will be nans, why MLForecast does not delete the first row? And to precede your question, no, my original dataset has no nans.Adrian Muntean
03/06/2023, 10:03 PMDaniel Turse
03/07/2023, 2:19 AMMerlin
03/07/2023, 3:09 PM