You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
A very interesting project, really appreciate it.
Got the below error while giving it a try, please help
<class 'pandas.core.frame.DataFrame'>
The data has been successfully parsed by infering a frequency, and establishing a 'Date' index and 'Target' column.
140
An insample split of training size 140 and testing size 47 has been constructed
I have experienced the same error when using sub-daily time series data such as hourly data etc. For example data with frequencies of the following: "3H", "5T" (minutes).
I didn’t see this issue and posted a new one this morning. I’m trying to use annual data (2007, 2008, etc) and I get the same error. I wonder if there is a way to specify the frequency and not have AtsPy infer it?
Hi,
A very interesting project, really appreciate it.
Got the below error while giving it a try, please help
<class 'pandas.core.frame.DataFrame'>
The data has been successfully parsed by infering a frequency, and establishing a 'Date' index and 'Target' column.
140
An insample split of training size 140 and testing size 47 has been constructed
UnboundLocalError Traceback (most recent call last)
in
----> 1 forecast_in, performance = am.forecast_insample(); forecast_in
~/anaconda3/envs/srtest/lib/python3.6/site-packages/atspy/init.py in forecast_insample(self)
845
846 def forecast_insample(self):
--> 847 models_dict, freq, test = self.train_insample()
848 forecast_len = test.shape[0]
849 forecast_dict = forecast_models(models_dict, forecast_len, freq,test, in_sample=True, GPU=self.GPU)
~/anaconda3/envs/srtest/lib/python3.6/site-packages/atspy/init.py in train_insample(self)
834 train, test = train_test_split(dataframe, train_proportion=0.75)
835 forecast_len = len(test)
--> 836 models, seasonal = train_models(train, models= self.model_list,forecast_len= forecast_len,full_df=dataframe,seasonality=self.season,in_sample=True,freq=freq, GPU=self.GPU )
837 self.seasonality = seasonal
838
~/anaconda3/envs/srtest/lib/python3.6/site-packages/atspy/init.py in train_models(train, models, forecast_len, full_df, seasonality, in_sample, freq, GPU)
317 seasons = select_seasonality(train, seasonality)
318
--> 319 periods = select_seasonality(train, 'periodocity')
320
321 models_dict = {}
~/anaconda3/envs/srtest/lib/python3.6/site-packages/atspy/init.py in select_seasonality(train, season)
149 def select_seasonality(train, season):
150 if season == "periodocity":
--> 151 seasonality = infer_periodocity(train)
152 elif season== "infer_from_data":
153 seasonality = infer_seasonality(train)
~/anaconda3/envs/srtest/lib/python3.6/site-packages/atspy/init.py in infer_periodocity(train)
145 periodocity = 1000
146
--> 147 return periodocity
148
149 def select_seasonality(train, season):
UnboundLocalError: local variable 'periodocity' referenced before assignment
The text was updated successfully, but these errors were encountered: