arp_timeseries_code.py

import arperiodogram as arp

ts = arp.TimeSeries(store_df[['visitors']].values,'1',split=0.7)
ts.season_num = 2
ts.lag_num = 2
ts.create_seasons()
ts.phase_correlation()
# ts.set_top_lags([1,2,3])
print("lag values:",ts.top_lags)
print("seasonal periods:",ts.periods)
ts.create_lags()
model = arp.AR_P(ts)
valid_y  = ts.get_valid_y()
train_y  = ts.get_train_y()
train_X = ts.get_train_X_season()
valid_X = ts.get_valid_X_season()
model.fit(train_X,train_y,kernel_transform=0.99,kernel_order=2)
y_pred = model.predict(train_X,train_y,is_train=True)
plt.figure(figsize=(10,10))
plt.plot(y_pred)
plt.plot(train_y)
plt.xlabel('index')
plt.ylabel('y')
plt.title("actual & predicted")
plt.show()