YEISON ARMANDO BUITRAGO LOPEZ
03/14/2022, 11:38 AMYEISON ARMANDO BUITRAGO LOPEZ
03/14/2022, 11:46 AMYEISON ARMANDO BUITRAGO LOPEZ
03/14/2022, 11:47 AMCristian (Nixtla)
03/14/2022, 2:21 PMCristian (Nixtla)
03/14/2022, 2:24 PMYEISON ARMANDO BUITRAGO LOPEZ
03/14/2022, 3:05 PMPandula
04/06/2022, 8:47 AMFabian Müller
04/11/2022, 9:24 PMTimeSeriesDataset
and WindowsDataset
. While the first one stores an entire time-series the later one stores sliding windows created from time-series, correct?
The second approach feels natural when working with nbeats-like models (as well as other neural forecasting models). However, the implementation you opted confuses me a bit:
Previously, when creating my own generators, I have chosen the following recipe for feeding data during training to the model:
1. Sample n
time-series from the dataset
2. For each series, sample a split time point
3. Create one window for each series form the sampled splits (resulting in n
windows) and feed them to model as a mini-batch (n = batch_size)
This is also the approach described in Oreshkina et al. 2020 (where it is combined with stratified sampling).
The implementation you chosen for the WindowsDataset
(at least as far as I understand it) is different in that all possible windows are generated for a series and retuned (resulting in n != batch_size
).
I am wondering:
1. Why did you choose this implementation style, what are the advantages?
2. Is it possible to implement the Oreshkina et al. 2020 style sampling (especially the stratified version) using your package?
Best,
Fabian
Oreshkina et al. 2020: https://arxiv.org/pdf/2009.11961.pdfDaniel Falbel
04/28/2022, 4:36 PMAmir Moeini
05/02/2022, 2:19 PMf_cols
is said to be used to identify future available exogenous variables. But I can't find anywhere in the whole code that f_cols
and f_idxs
are actually used. How are exogenous variables that are available in the future and the ones that are not, treated differently?Andre P
05/11/2022, 10:03 PMAndre P
05/11/2022, 10:03 PMAndre P
05/11/2022, 10:05 PMAssertionError: Mismatch in X, Y ds
when performing the following step of "getting_started.ipynb"
Y_df_forecast = model.forecast(Y_df_train, X_df= X_df)
Y_df_forecast.rename(columns={'y': 'y_hat'}, inplace=True)
Y_df_forecast.head()
I checked if Y_df_test had the ds
correctly following Y_df_train and it has.Andre P
05/11/2022, 10:05 PMKin Gtz. Olivares
05/11/2022, 10:52 PMY_df
and X_df
, for the moment N-BEATSx only operates with exogenous data available at the time of the prediction. For example: calendar variables, predictions of other series.
Can you check if len(X_df)==len(Y_df)+H
, where H is the forecast horizon (n_time_out)?Andre P
05/12/2022, 7:37 AMlen(X_df)==len(Y_df)+H
.Andre P
05/12/2022, 10:27 AMAndre P
05/12/2022, 1:35 PMrariwa
05/16/2022, 4:50 PMChris Gervais
05/25/2022, 2:35 PMS_df
dataframe? i'm on v0.0.9
and it asks for:
S_df: pd.DataFrame
Static exogenous variables with columns ['unique_id', 'ds'] and static variables.
i assumed it would look similar to the Y_df
dataframe, with columns for unique_id, ds, y
, but on .fit
it throws the following error:
ValueError('Found duplicated unique_ids in S_df')
i think because we have unique_id, ds
as a time series in S_df
, any ideas on what i'm missing?Patricio
05/27/2022, 2:40 PMChris Gervais
05/31/2022, 2:33 PMhyperopt
suggests a new model config? and if so, are each of those loaders guaranteed to contain the same data splits?Chris Gervais
06/06/2022, 3:06 PMmodel_fit_predict
in the same format as model.forecast
?Kin Gtz. Olivares
06/06/2022, 3:09 PMrariwa
06/21/2022, 3:31 PMrariwa
06/21/2022, 3:31 PMrariwa
06/21/2022, 3:33 PMMusa
07/15/2022, 10:22 PMMusa
07/15/2022, 10:26 PMTypeError: sort_values() got an unexpected keyword argument 'ignore_index'
any idea what is the issue?
Thanks.Kin Gtz. Olivares
07/15/2022, 10:28 PM