# plt.title(filename)
# plt.show()
OUTLIER_TYPES.append(to_include[filename][2])
start = time.time()
NUMBER_OF_MISSING_TIME_STEPS.append(ts.get_how_many_miss())
ts.set_miss(fill=False)
end = time.time()
MISSING_TIMES.append(end - start)
FROM_NUMENTA.append(to_include[filename][3])
start = time.time()
ts.set_seasonality()
end = time.time()
SEASONALITIES.append(ts.get_seasonality())
PERIODICITIES.append(ts.get_period())
SEASONALITY_TIMES.append(end - start)
start = time.time()
ts.set_trend()
end = time.time()
TRENDS.append(ts.get_trend())
TREND_TYPES.append(ts.get_trend_type())
def sarimax(ts_obj,
gaussian_window_size,
step_size,
plot_anomaly_score=False,
plot_forecast=False):
slide_size = 200
if ts_obj.get_length() >= slide_size:
n = slide_size
list_df = [
ts_obj.dataframe[i:i + n]
for i in range(0, ts_obj.dataframe.shape[0], n)
]
anomaly_scores_list = []
times_list = []
forecasts_list = []
for chunk_df in tqdm(list_df):
print(ts_obj.name)
if len(chunk_df) >= slide_size:
chunk_ts_obj = TimeSeries(chunk_df,
timestep=ts_obj.timestep,
dateformat=ts_obj.dateformat,
name=ts_obj.name)
# NEED TO SET CHARACTERISTIC OF SEASONALITY ONLY
chunk_ts_obj.set_seasonality()
chunk_result = sarimax_mini(chunk_ts_obj,
gaussian_window_size,
step_size,
plot_anomaly_score=False,
plot_forecast=False)
anomaly_scores_list.append(chunk_result["Anomaly Scores"])
times_list.append(chunk_result["Time"])
forecasts_list.append(chunk_result["Forecast"])
anomaly_scores = []
for sublist in anomaly_scores_list:
for item in sublist:
anomaly_scores.append(item)
forecast = []
for sublist in forecasts_list:
for item in sublist:
forecast.append(item)
while len(anomaly_scores) < ts_obj.get_length():
anomaly_scores.append(0)
while len(forecast) < ts_obj.get_length():
forecast.append(0)
if plot_forecast:
plt.plot(forecast, alpha=.7, label="Predictions")
plt.plot(ts_obj.dataframe["value"].values, alpha=.5, label="Data")
plt.legend()
plt.show()
if plot_anomaly_score:
plt.subplot(211)
plt.title("Anomaly Scores")
plt.plot(anomaly_scores)
plt.ylim([.99, 1])
plt.subplot(212)
plt.title("Time Series")
plt.plot(ts_obj.dataframe["value"].values)
plt.axvline(ts_obj.get_probationary_index(),
color="black",
label="probationary line")
plt.tight_layout()
plt.show()
return {
"Anomaly Scores": np.asarray(anomaly_scores),
"Time": sum(times_list),
"Forecast": forecast
}
else:
return sarimax_mini(ts_obj,
gaussian_window_size,
step_size,
plot_anomaly_score=plot_anomaly_score,
plot_forecast=plot_forecast)
