I have a one month data that is on a daily basis.It captures cpu utilization data everyday.I want to produce some forecast results.I have split the data into two parts train- which takes first 15 days and test which takes the last 16 days and on this I want to do a forecast and compare the forecast result with the given last 16 days result.So far I have tried various implementations such as moving average, simple exponential smoothing.Now I want to try something more complex and accurate such as Holt-Winters Method and ARIMA model.Below is the result that I get for Holt's Linear Trend method which takes into account trend and seasonality.
Now I want to implement Holts Winter method which is one of the preferred forecasting technique.Here is the code below
# get the first 15 days
df_train = psql.read_sql("SELECT date,cpu FROM {} where date between '{}' and '{} 23:59:59';".format(conf_list[1], '2018-03-02', '2018-03-16'), conn).fillna(0)
df_train["date"] = pd.to_datetime(df_train["date"], format="%m-%d-%Y")
df_train.set_index("date", inplace=True)
df_train = df_train.resample('D').mean().fillna(0)
# get the last 15 days
df_test = psql.read_sql("SELECT date,cpu FROM {} where date between '{}' and '{} 23:59:59';".format(conf_list[1], '2018-03-18', '2018-03-31'), conn).fillna(0)
df_test["date"] = pd.to_datetime(df_test["date"], format="%m-%d-%Y")
df_test.set_index("date", inplace=True)
df_test = df_test.resample('D').mean().fillna(0)
Here is the code for Holt's Winter method
y_hat_avg = df_test.copy()
fit1 = ExponentialSmoothing(np.asarray(df_train['cpu']), seasonal_periods=1, trend='add', seasonal='add',).fit()
y_hat_avg['Holt_Winter'] = fit1.forecast(len(df_test))
plt.figure(figsize=(16,8))
plt.plot(df_train['cpu'], label='Train')
plt.plot(df_test['cpu'], label='Test')
plt.plot(y_hat_avg['Holt_Winter'], label='Holt_Winter')
plt.legend(loc='best')
plt.show()
Now I am getting an error for the seasonal_periods parameter.It accepts an integer and I believe it accepts month as a value.Even in their documentation, they only refer to as no of seasons http://www.statsmodels.org/dev/generated/statsmodels.tsa.holtwinters.ExponentialSmoothing.html#statsmodels.tsa.holtwinters.ExponentialSmoothing
Now since I have only 1 month of data out which I want to run forecast on first 15 days, what season value should I pass?Assuming seasons refer to months, ideally it should be 0.5 (15 days), but it only accepts integers.If I pass the value as 1, I get the below error
Traceback (most recent call last):
File "/home/souvik/PycharmProjects/Pandas/forecast_health.py", line 89, in <module>
fit1 = ExponentialSmoothing(np.asarray(df_train['cpu']), seasonal_periods=1, trend='add', seasonal='add',).fit()
File "/home/souvik/data_analysis/lib/python3.5/site-packages/statsmodels/tsa/holtwinters.py", line 571, in fit
Ns=20, full_output=True, finish=None)
File "/home/souvik/data_analysis/lib/python3.5/site-packages/scipy/optimize/optimize.py", line 2831, in brute
Jout = vecfunc(*grid)
File "/home/souvik/data_analysis/lib/python3.5/site-packages/numpy/lib/function_base.py", line 2755, in __call__
return self._vectorize_call(func=func, args=vargs)
File "/home/souvik/data_analysis/lib/python3.5/site-packages/numpy/lib/function_base.py", line 2831, in _vectorize_call
outputs = ufunc(*inputs)
File "/home/souvik/data_analysis/lib/python3.5/site-packages/scipy/optimize/optimize.py", line 2825, in _scalarfunc
return func(params, *args)
File "/home/souvik/data_analysis/lib/python3.5/site-packages/statsmodels/tsa/holtwinters.py", line 207, in _holt_win_add_add_dam
return sqeuclidean((l + phi * b) + s[:-(m - 1)], y)
ValueError: operands could not be broadcast together with shapes (16,) (0,)
If I pass the paramter as None, I get the below error
Traceback (most recent call last):
File "/home/souvik/PycharmProjects/Pandas/forecast_health.py", line 89, in <module>
fit1 = ExponentialSmoothing(np.asarray(df_train['cpu']), seasonal_periods=None, trend='add', seasonal='add',).fit()
File "/home/souvik/data_analysis/lib/python3.5/site-packages/statsmodels/tsa/holtwinters.py", line 399, in __init__
'Unable to detect season automatically')
NotImplementedError: Unable to detect season automatically
How do I get the forecast for the last 16 days of a month with Holt-Winters method?What am I doing wrong?
Here is the data for the month if anyone wants to reproduce the results
cpu
date
2018-03-01 00:00:00+00:00 1.060606
2018-03-02 00:00:00+00:00 1.014035
2018-03-03 00:00:00+00:00 1.048611
2018-03-04 00:00:00+00:00 1.493392
2018-03-05 00:00:00+00:00 3.588957
2018-03-06 00:00:00+00:00 2.500000
2018-03-07 00:00:00+00:00 5.265306
2018-03-08 00:00:00+00:00 0.000000
2018-03-09 00:00:00+00:00 3.062099
2018-03-10 00:00:00+00:00 5.861751
2018-03-11 00:00:00+00:00 0.000000
2018-03-12 00:00:00+00:00 0.000000
2018-03-13 00:00:00+00:00 7.235294
2018-03-14 00:00:00+00:00 4.011662
2018-03-15 00:00:00+00:00 3.777409
2018-03-16 00:00:00+00:00 5.754559
2018-03-17 00:00:00+00:00 4.273390
2018-03-18 00:00:00+00:00 2.328782
2018-03-19 00:00:00+00:00 3.106048
2018-03-20 00:00:00+00:00 5.584877
2018-03-21 00:00:00+00:00 9.869841
2018-03-22 00:00:00+00:00 5.588215
2018-03-23 00:00:00+00:00 3.620377
2018-03-24 00:00:00+00:00 3.468021
2018-03-25 00:00:00+00:00 2.605649
2018-03-26 00:00:00+00:00 3.670559
2018-03-27 00:00:00+00:00 4.071777
2018-03-28 00:00:00+00:00 4.159690
2018-03-29 00:00:00+00:00 4.364939
2018-03-30 00:00:00+00:00 4.743253
2018-03-31 00:00:00+00:00 4.928571
