def main(alpha, beta, gama):
series = read_csv('/home/akshit/Documents/Downloads/2.csv',
header=0,
index_col=0,
squeeze=True)
series.columns = ['a', 'b', 'c', 'd']
series['e'] = pow((series['a'] * series['a'] + series['b'] * series['b'] +
series['c'] * series['c']), 0.5)
df = pd.DataFrame({'$a': series['e']})
fd = np.array(df)
plt.plot(fd)
model = ExponentialSmoothing(fd)
alpha = float(alpha)
beta = float(beta)
gama = float(gama)
a = ExponentialSmoothing.fit(model,
smoothing_level=alpha,
smoothing_slope=beta,
use_boxcox=True,
remove_bias=True,
smoothing_seasonal=gama)
c = HoltWintersResults.predict(a, start=0)
plt.plot(c)
plt.show()
def main(alpha, beta, gama):
abspath = os.path.abspath(os.curdir)
series = read_csv(abspath + '/2.csv', header=0, index_col=0, squeeze=True)
series.columns = ['a', 'b', 'c', 'd']
series['e'] = pow((series['a'] * series['a'] + series['b'] * series['b'] +
series['c'] * series['c']), 0.5)
df = pd.DataFrame({'$a': series['e']})
fd = np.array(df)
plt.plot(fd)
model = ExponentialSmoothing(fd)
alpha = float(alpha)
beta = float(beta)
gama = float(gama)
a = ExponentialSmoothing.fit(model,
smoothing_level=alpha,
smoothing_slope=beta,
use_boxcox=True,
remove_bias=True,
smoothing_seasonal=gama)
c = HoltWintersResults.predict(a, start=0)
plt.plot(c)
plt.xlabel('Data', fontsize=12)
plt.ylabel('Indices', fontsize=12)
plt.show()
def score(self, ts: TestTS, model: HoltWintersResults):
predicted_values = model.predict(ts.start, ts.end)
return {
'MAE': find_mae(ts.data.values, predicted_values.values),
"MAPE": find_mape(ts.data.values, predicted_values.values),
'AIC': model.aic,
'AICC': model.aicc,
'BIC': model.bic,
'SSE': model.sse
}
def brutlag_algorithm(ts: TimeSeries,
model: HoltWintersResults,
tolerance: int = 0.5,
period=12,
sf=2,
gamma=0.3684211):
LOG.warning(
'This function in future release will be refactored. '
'Hence use this with care expecting breaking changes in upcoming versions.'
)
LOG.info('Performing brutlag anomaly detection ...')
# period - seasonal_period of the given time series
# gamma - the seasonality component (0.2 -0.5)
# sf - brutlag scaling factor for the confidence bands. (2-3)
ub = [] # upper bound or upper confidence band
lb = [] # lower bound or lower confidence band
prediction = model.predict(ts.start, ts.end)
difference_array = []
dt = []
difference_table = {
"actual": ts.data,
"predicted": prediction,
"difference": difference_array,
"UB": ub,
"LB": lb
}
"""Calculation of confidence bands using brutlag algorithm"""
for i in range(len(prediction)):
diff = ts.data[i] - prediction[i]
if i < period:
dt.append(gamma * abs(diff))
else:
dt.append(gamma * abs(diff) + (1 - gamma) * dt[i - period])
difference_array.append(diff)
ub.append(prediction[i] + sf * dt[i])
lb.append(prediction[i] - sf * dt[i])
print("\nDifference between actual and predicted\n")
difference = pd.DataFrame(difference_table)
print(difference)
"""Classification of data points as either normal or anomaly"""
normal = []
normal_date = []
anomaly = []
anomaly_date = []
actual = []
for i in range(len(ts.data.index)):
if (ub[i] <= ts.data[i] or lb[i] >= ts.data[i]
) and i > period and abs(ts.data[i] - prediction[i]) > tolerance:
anomaly_date.append(ts.data.index[i])
anomaly.append(ts.data[i])
actual.append(prediction[i])
else:
normal_date.append(ts.data.index[i])
normal.append(ts.data[i])
anomaly = pd.DataFrame({
"date": anomaly_date,
"observed": anomaly,
'expected': actual
})
anomaly.set_index('date', inplace=True)
normal = pd.DataFrame({"date": normal_date, "value": normal})
normal.set_index('date', inplace=True)
print("\nThe data points classified as anomaly\n")
print(anomaly)
plot_anomaly(ts, anomaly, normal, lb, ub)
return anomaly
def predict(self, model: HoltWintersResults, start, end):
return model.predict(start, end)
plt.show()
plt.plot(df_test, predictions,'+')
plt.xlabel('Original Values')
plt.ylabel('Predicted Values')
plt.title('Predicted vs. Original Values')
plt.show()
print(len(df_train))
#--------------------------------------------------TASK 3---------------------------------------------------#
a_arr = []
rmse_arr = []
for a in np.arange(0.1, 1.0, 0.1):
a_arr.append(a)
ema_model = SimpleExpSmoothing(df_train).fit(smoothing_level=a, optimized=False)
result = HoltWintersResults.predict(ema_model,start=0)
mse = mean_squared_error(df_train, result)
rmse = np.sqrt(mean_squared_error(df_train, result))
rmse_arr.append(rmse)
plt.plot(df_train,label='Original')
plt.plot(result, color='red', label='Predictions')
plt.legend(loc='best')
plt.xlabel('t')
plt.show()
plt.plot(df_train[0:100], label='Original')
plt.plot(result[0:100], color='red', label='Predictions')
plt.legend(loc='best')
plt.xlabel('t')
plt.show()
print('a:',a)
print("RMSE:", rmse)