I am currently using the Statsmodels library for forecasting. I am trying to run triple exponential smoothing with seasonality, trend, and a smoothing factor, but keep getting errors. Here is a sample pandas series with the frequency already set:
2018-01-01 25
2018-02-01 30
2018-03-01 40
2018-04-01 38
2018-05-01 33
2018-06-01 36
2018-07-01 34
2018-08-01 35
2018-09-01 37
2018-10-01 41
2018-11-01 36
2018-12-01 32
2019-01-01 31
2019-02-01 29
2019-03-01 28
2019-04-01 29
2019-05-01 30
Freq: MS, dtype:float64
Here is my code:
import pandas as pd
from statsmodels.tsa.holtwinters import ExponentialSmoothing, SimpleExpSmoothing
def triple_expo(input_data_set, output_list1):
data = input_data_set
model1 = ExponentialSmoothing(data, trend='add', seasonal='mul', damped=False, seasonal_periods=12, freq='M').fit(smoothing_level=0.1, smoothing_slope=0.1, smoothing_seasonal=0.1, optimized=False)
fcast1 = model1.forecast(12)
fcast_list1 = list(fcast1)
output_list1.append(fcast_list1)
for product in unique_products:
product_slice = sorted_product_df["Product"] == product
unique_slice = sorted_product_df[product_slice]
amount = unique_slice["Amount"].tolist()
index = unique_slice["Date"].tolist()
unique_series = pd.Series(amount, index)
unique_series.index = pd.DatetimeIndex(unique_series.index, freq=pd.infer_freq(unique_series.index))
triple_expo(unique_series, triple_out_one)
I originally did not have a frequency argument at all because I was following the example on the statsmodels website which is here: http://www.statsmodels.org/stable/examples/notebooks/generated/exponential_smoothing.html
They did not pass a frequency argument at all as they inferred it using the DateTimeIndex in pandas. When I have no frequency argument, my error is "operands could not be broadcast together with shapes <5,><12,>". I have 17 months of data and pandas recognizes it as 'Ms'. The 5 and the 12 is referring to the 17. I then pass freq='M' like I have in the code sample, and I get "The given frequency argument is incompatible with the given index". I then try setting it to everything from len(data) to len(data)-1 and always get errors. I tried the len(data)-1 because I was originally referencing this stack post: seasonal_decompose: operands could not be broadcast together with shapes on a series
In that post, he said it would work if you set the frequency to one less than the length of the data set. It does not work for me though. Any help would be appreciated. Thanks!
EDIT: The comment below suggested I include more than one year's worth of data and it worked when I did that.
