00db6a30ad
- Refactoring generate_flrg and train methods - Introducing batches and model saving on fit method
120 lines
3.2 KiB
Python
120 lines
3.2 KiB
Python
#!/usr/bin/python
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# -*- coding: utf8 -*-
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import os
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import numpy as np
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import pandas as pd
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from pyFTS.partitioners import Grid
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from pyFTS.common import Transformations
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from pyFTS import hofts, song, yu, cheng, ismailefendi, sadaei, hwang
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from pyFTS.models import chen
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from pyFTS.models.ensemble import ensemble
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os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
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diff = Transformations.Differential(1)
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passengers = pd.read_csv("DataSets/AirPassengers.csv", sep=",")
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passengers = np.array(passengers["Passengers"])
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e = ensemble.AllMethodEnsembleFTS(alpha=0.25, point_method="median", interval_method='quantile')
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fo_methods = [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, cheng.TrendWeightedFTS, sadaei.ExponentialyWeightedFTS,
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ismailefendi.ImprovedWeightedFTS]
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ho_methods = [hofts.HighOrderFTS, hwang.HighOrderFTS]
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fs = Grid.GridPartitioner(passengers, 10, transformation=diff)
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e.append_transformation(diff)
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e.train(passengers, fs.sets, order=3)
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"""
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for method in fo_methods:
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model = method("")
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model.append_transformation(diff)
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model.train(passengers, fs.sets)
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e.append_model(model)
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for method in ho_methods:
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for order in [1,2,3]:
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model = method("")
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model.append_transformation(diff)
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model.train(passengers, fs.sets, order=order)
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e.append_model(model)
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arima100 = arima.ARIMA("", alpha=0.25)
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#tmp.append_transformation(diff)
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arima100.train(passengers, None, order=(1,0,0))
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arima101 = arima.ARIMA("", alpha=0.25)
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#tmp.append_transformation(diff)
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arima101.train(passengers, None, order=(1,0,1))
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arima200 = arima.ARIMA("", alpha=0.25)
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#tmp.append_transformation(diff)
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arima200.train(passengers, None, order=(2,0,0))
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arima201 = arima.ARIMA("", alpha=0.25)
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#tmp.append_transformation(diff)
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arima201.train(passengers, None, order=(2,0,1))
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e.append_model(arima100)
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e.append_model(arima101)
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e.append_model(arima200)
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e.append_model(arima201)
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e.train(passengers, None)
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_mean = e.forecast(passengers, method="mean")
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print(_mean)
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_median = e.forecast(passengers, method="median")
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print(_median)
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"""
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"""
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_extremum = e.forecast_interval(passengers, method="extremum")
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print(_extremum)
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_quantile = e.forecast_interval(passengers, method="quantile", alpha=0.25)
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print(_quantile)
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_normal = e.forecast_interval(passengers, method="normal", alpha=0.25)
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print(_normal)
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"""
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#"""
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_extremum = e.forecast_ahead_interval(passengers, 10, method="extremum")
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print(_extremum)
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_quantile = e.forecast_ahead_interval(passengers[:50], 10, method="quantile", alpha=0.05)
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print(_quantile)
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_quantile = e.forecast_ahead_interval(passengers[:50], 10, method="quantile", alpha=0.25)
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print(_quantile)
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_normal = e.forecast_ahead_interval(passengers[:50], 10, method="normal", alpha=0.05)
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print(_normal)
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_normal = e.forecast_ahead_interval(passengers[:50], 10, method="normal", alpha=0.25)
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print(_normal)
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#"""
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#dist = e.forecast_ahead_distribution(passengers, 20)
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#print(dist)
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#bchmk.plot_compared_intervals_ahead(passengers[:120],[e], ['blue','red'],
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# distributions=[True,False], save=True, file="pictures/distribution_ahead_arma",
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# time_from=60, time_to=10, tam=[12,5])
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