def simpleExponentialSmoothing(df,
no_predictions=7,
debug=False,
visualize=False):
train_data, test_data = df[1:int(len(df) - no_predictions
)], df[int(len(df) - no_predictions):]
fit1 = SimpleExpSmoothing(np.asarray(train_data)).fit(smoothing_level=0.85,
optimized=False)
if (debug):
print(fit1.summary())
predictions = fit1.forecast(no_predictions * 2)
if (visualize):
plt.plot(list(test_data), color='blue', label='testing data')
plt.plot(list(predictions), color='red', label='prediction')
plt.legend(loc='upper left', fontsize=8)
plt.show()
error = np.sqrt(mean_squared_error(test_data,
predictions[:no_predictions]))
return (predictions[-no_predictions:], error, fit1)
import math
from statistics import mean
plt.style.use('fivethirtyeight')
df = pd.read_excel("../00Daily/Egypt.xlsx", squeeze=True, parse_dates=True)
df = df[["Date", "LocalTransmission"]]
df.set_index("Date", inplace=True)
df.dropna(inplace=True)
##df['Date'] = pd.to_datetime(df['Date'])
LocalTransmission = df['LocalTransmission'].astype('int32')
#print (df.head())
print(df.index)
result = SimpleExpSmoothing(df).fit()
print(result.summary())
#print(result.params)
predictions = result.predict(start="2020-03-01", end="2020-05-01")
#accuracy = result.score()
print("Predictions: ", predictions, sep='\n')
##accuracy = result.score()
#print (accuracy)
#result.plot_predict(start="2020-03-01", end="2020-05-01")
plt.plot(predictions)
plt.show()
##def mean_forecast_error(y, yhat):
## return y.sub(yhat).mean()
rain_mod1_pred = pd.DataFrame(rain_mod1_pred,
index=pd.date_range(start='1913-01-01',
periods=8,
freq='YS'))
rain_mod1_pred
#Plot Actual & forecast
plt.plot(rain)
plt.plot(rain_mod1_pred)
plt.legend(['Actual', 'Forecast'], bbox_to_anchor=(1, 1), loc=2)
plt.show()
#Model with single exponential smoothing
from statsmodels.tsa.holtwinters import SimpleExpSmoothing
rain_ses = SimpleExpSmoothing(rain).fit()
rain_ses.summary()
''' SimpleExpSmoothing Model Results
==============================================================================
Dep. Variable: rain No. Observations: 100
Model: SimpleExpSmoothing SSE 1758.465
Optimized: True AIC 290.703
Trend: None BIC 295.913
Seasonal: None AICC 291.124
Seasonal Periods: None Date: Fri, 26 Mar 2021
Box-Cox: False Time: 00:58:24
Box-Cox Coeff.: None
==============================================================================
coeff code optimized
------------------------------------------------------------------------------
smoothing_level 1.4901e-08 alpha True
initial_level 24.823898 l.0 True
) #First 6 and last 6 values are Na's due calculation of seasonality indices of 12 months
births_dec_a.seasonal
births_dec_a.resid.head(
20
) #First 6 and last 6 values are Na's due calculation of seasonality indices of 12 months
'''
SimpleExpSmoothing is used when there is no trend and Seasonal
Holt is used when there is trend
ExponentialSmoothing is when there trend and seasonal'''
#For this data supposed to use last method but trying other methods for practice
#Model with single exponential smoothing
from statsmodels.tsa.holtwinters import SimpleExpSmoothing
births_ses = SimpleExpSmoothing(births).fit()
births_ses.summary()
'''
SimpleExpSmoothing Model Results
==============================================================================
Dep. Variable: births No. Observations: 168
Model: SimpleExpSmoothing SSE 280.315
Optimized: True AIC 90.007
Trend: None BIC 96.255
Seasonal: None AICC 90.253
Seasonal Periods: None Date: Fri, 26 Mar 2021
Box-Cox: False Time: 00:21:27
Box-Cox Coeff.: None
==============================================================================
coeff code optimized
------------------------------------------------------------------------------
smoothing_level 0.4379774 alpha True
'''
ADF Statistic: -1.614915
p-value: 0.475396
Critical Values:
1%: -3.610
5%: -2.939
10%: -2.608'''
#p-value: 0.475396 ie > 0.5, Null Hypothesis accepted, Data is not stationary
#H0: Data is not stationary
#Being trend and seasonality is not visible in the data using
#single exponential smoothing
from statsmodels.tsa.holtwinters import SimpleExpSmoothing
skirts_ses = SimpleExpSmoothing(skirts).fit()
skirts_ses.summary()
'''
SimpleExpSmoothing Model Results
==============================================================================
Dep. Variable: diam No. Observations: 46
Model: SimpleExpSmoothing SSE 41869.930
Optimized: True AIC 317.429
Trend: None BIC 321.087
Seasonal: None AICC 318.405
Seasonal Periods: None Date: Thu, 25 Mar 2021
Box-Cox: False Time: 23:09:03
Box-Cox Coeff.: None
==============================================================================
coeff code optimized
------------------------------------------------------------------------------
smoothing_level 0.9950000 alpha True
Train = bluejet.head(132)
Test = bluejet.tail(12)
# Creating a function to calculate the MAPE value for test data
def MAPE(pred, org):
temp = np.abs((pred - org) / org) * 100
return np.mean(temp)
Train["flyers"] = Train.flyers.astype('double')
bluejet["flyers"] = bluejet.flyers.astype('double')
# Simple Exponential Method
help(SimpleExpSmoothing)
ses_model = SimpleExpSmoothing(Train["flyers"]).fit()
ses_model.summary()
pred_ses = ses_model.predict(start=Test.index[0], end=Test.index[-1])
MAPE(pred_ses, Test.flyers) #14.25
# Holt method
hw_model = Holt(Train["flyers"]).fit()
hw_model.summary()
pred_hw = hw_model.predict(start=Test.index[0], end=Test.index[-1])
MAPE(pred_hw, Test.flyers) #12.41
# Holts winter exponential smoothing with additive seasonality
hwe_model_add_add = ExponentialSmoothing(Train["flyers"],
seasonal="add",
trend="add",
seasonal_periods=12).fit()
hwe_model_add_add.summary()
