ValueError help with Simple Exponential Smoothing analysis on my data set

+1 vote

I'm very new, and attempting to teach myself Python through online resources.

I've attempted the following code I found online to conduct a Single Exponential Smoothing Analysis on my time series data:

# Simple Exponential Smoothing
fit1 = SimpleExpSmoothing(Data).fit(smoothing_level=0.2,optimized=False)
fcast1 = fit1.forecast(12).rename(r'$\alpha=0.2$')
# plot
fcast1.plot(marker='o', color='blue', legend=True)
fit1.fittedvalues.plot(marker='o',  color='blue')

fit2 = SimpleExpSmoothing(Data).fit(smoothing_level=0.6,optimized=False)
fcast2 = fit2.forecast(12).rename(r'$\alpha=0.6$')
# plot
fcast2.plot(marker='o', color='red', legend=True)
fit2.fittedvalues.plot(marker='o', color='red')

fit3 = SimpleExpSmoothing(Data).fit()
fcast3 = fit3.forecast(12).rename(r'$\alpha=%s$'%fit3.model.params['smoothing_level'])
# plot
fcast3.plot(marker='o', color='green', legend=True)
fit3.fittedvalues.plot(marker='o', color='green')

This code returns the error: 

"Pandas data cast to numpy dtype of object. Check input data with np.asarray(data)."

I have researched online and attempted to create dummy variables within my dataset, however the result remains the same.

Month Product Key Income



01-Apr-18 A101 8289.05
01-Apr-18 A102 0.00

Above is a 3 row snippet of the dataset, could it be the structure of the data that causes the forecast/SES code to result in an error?

Entire ValueError code:

ValueError Traceback (most recent call last) <ipython-input-6-c73edf2e1e6d> in <module> 1 # Simple Exponential Smoothing ----> 2 fit1 = SimpleExpSmoothing(Data).fit(smoothing_level=0.2,optimized=False) 3 fcast1 = fit1.forecast(12).rename(r'$\alpha=0.2$') 4 # plot 5 fcast1.plot(marker='o', color='blue', legend=True) 

~\Anaconda3\lib\site-packages\statsmodels\tsa\ in __init__(self, endog) 1005 1006 def __init__(self, endog): -> 1007 super(SimpleExpSmoothing, self).__init__(endog) 1008 1009 def fit(self, smoothing_level=None, optimized=True, start_params=None, 

~\Anaconda3\lib\site-packages\statsmodels\tsa\ in __init__(self, endog, trend, damped, seasonal, seasonal_periods, dates, freq, missing) 484 seasonal_periods=None, dates=None, freq=None, missing='none'): 485 super(ExponentialSmoothing, self).__init__( --> 486 endog, None, dates, freq, missing=missing) 487 if trend in ['additive', 'multiplicative']: 488 trend = {'additive': 'add', 'multiplicative': 'mul'}[trend] 

~\Anaconda3\lib\site-packages\statsmodels\tsa\base\ in __init__(self, endog, exog, dates, freq, missing, **kwargs) 47 missing='none', **kwargs): 48 super(TimeSeriesModel, self).__init__(endog, exog, missing=missing, ---> 49 **kwargs) 50 51 # Date handling in indexes ~\Anaconda3\lib\site-packages\statsmodels\base\ in __init__(self, endog, exog, **kwargs) 214 215 def __init__(self, endog, exog=None, **kwargs): --> 216 super(LikelihoodModel, self).__init__(endog, exog, **kwargs) 217 self.initialize() 218 ~\Anaconda3\lib\site-packages\statsmodels\base\ in __init__(self, endog, exog, **kwargs) 66 hasconst = kwargs.pop('hasconst', None) 67 = self._handle_data(endog, exog, missing, hasconst, ---> 68 **kwargs) 69 self.k_constant = 70 self.exog = 

~\Anaconda3\lib\site-packages\statsmodels\base\ in _handle_data(self, endog, exog, missing, hasconst, **kwargs) 89 90 def _handle_data(self, endog, exog, missing, hasconst, **kwargs): ---> 91 data = handle_data(endog, exog, missing, hasconst, **kwargs) 92 # kwargs arrays could have changed, easier to just attach here 93 for key in kwargs: 

~\Anaconda3\lib\site-packages\statsmodels\base\ in handle_data(endog, exog, missing, hasconst, **kwargs) 633 klass = handle_data_class_factory(endog, exog) 634 return klass(endog, exog=exog, missing=missing, hasconst=hasconst, --> 635 **kwargs) ~\Anaconda3\lib\site-packages\statsmodels\base\ in __init__(self, endog, exog, missing, hasconst, **kwargs) 74 self.orig_endog = endog 75 self.orig_exog = exog ---> 76 self.endog, self.exog = self._convert_endog_exog(endog, exog) 77 78 self.const_idx = None 

~\Anaconda3\lib\site-packages\statsmodels\base\ in _convert_endog_exog(self, endog, exog) 473 exog = exog if exog is None else np.asarray(exog) 474 if endog.dtype == object or exog is not None and exog.dtype == object: --> 475 raise ValueError("Pandas data cast to numpy dtype of object. " 476 "Check input data with np.asarray(data).") 477 return super(PandasData, self)._convert_endog_exog(endog, exog) 

ValueError: Pandas data cast to numpy dtype of object. Check input data with np.asarray(data).
Jul 30, 2019 in Python by Declan

edited Jul 31, 2019 1,332 views
Hey @Declan, what form is your data in? Is it in the form of DataFrame format?
Hi, I'm currently using Jupyter not PyCharm and no I haven't used the DataFrame format, the example I was following didn't indicate to apply this.

I will try it now and see if it changes anything
@Kyraa I have just checked and using the "PD.READ.CSV" function creates a dataframe from the source data, if this is correct then the format is in a dataframe
@Kyraa, I think I have found the problem, I attempted to use some similar time series forecast through Power BI with the same dataset, and it returned with 'not enough data points'. I'm unsure how many points is required for the SES model, so I'll attempt the same code with another set holding more data points, to see if the result changes.


Yeah, try using a dataset with higher data points. Also, try converting your model as below before using the .fit()

fit2 = SimpleExpSmoothing(np.asarray(Data)).fit(smoothing_level=0.6,optimized=False)

Let me know what happens. 

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