https://github.com/antoinecarme/pyaf
Tip revision: d4e11a72635ea2b37cadfac2aba887df0ca0ee6b authored by Antoine Carme on 04 April 2020, 16:12:21 UTC
Some package dependencies are optional #117
Some package dependencies are optional #117
Tip revision: d4e11a7
test_ozone_no_models_enabled.py
import pandas as pd
import numpy as np
import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds
#get_ipython().magic('matplotlib inline')
b1 = tsds.load_ozone()
df = b1.mPastData
#df.tail(10)
#df[:-10].tail()
#df[:-10:-1]
#df.describe()
lEngine = autof.cForecastEngine()
lEngine
H = b1.mHorizon;
# lEngine.mOptions.enable_slow_mode();
# lEngine.mOptions.mDebugPerformance = True;
lEngine.mOptions.disable_all_transformations();
lEngine.mOptions.disable_all_trends();
lEngine.mOptions.disable_all_periodics();
lEngine.mOptions.disable_all_autoregressions();
lEngine.train(df , b1.mTimeVar , b1.mSignalVar, H);
lEngine.getModelInfo();
print(lEngine.mSignalDecomposition.mTrPerfDetails.head());
lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mResolution
lEngine.standardPlots("outputs/my_ozone");
dfapp_in = df.copy();
dfapp_in.tail()
#H = 12
dfapp_out = lEngine.forecast(dfapp_in, H);
#dfapp_out.to_csv("outputs/ozone_apply_out.csv")
dfapp_out.tail(2 * H)
print("Forecast Columns " , dfapp_out.columns);
Forecast_DF = dfapp_out[[b1.mTimeVar , b1.mSignalVar, b1.mSignalVar + '_Forecast']]
print(Forecast_DF.info())
print("Forecasts\n" , Forecast_DF.tail(H));
print("\n\n<ModelInfo>")
print(lEngine.to_json());
print("</ModelInfo>\n\n")
print("\n\n<Forecast>")
print(Forecast_DF.tail(2*H).to_json(date_format='iso'))
print("</Forecast>\n\n")