271 lines
11 KiB
Python
271 lines
11 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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import matplotlib as plt
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import matplotlib.pyplot as plt
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#from mpl_toolkits.mplot3d import Axes3D
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import pandas as pd
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from pyFTS.partitioners import Grid, Entropy, FCM, Huarng
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from pyFTS.common import FLR,FuzzySet,Membership,Transformations
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from pyFTS import fts,hofts,ifts,pwfts,tree, chen
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#from pyFTS.benchmarks import benchmarks as bchmk
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from pyFTS.benchmarks import naive, arima
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from pyFTS.benchmarks import Measures
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from numpy import random
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from pyFTS.models.seasonal import SeasonalIndexer
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os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
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diff = Transformations.Differential(1)
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ix = SeasonalIndexer.LinearSeasonalIndexer([12, 24], [720, 1],[False, False])
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"""
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DATASETS
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"""
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#enrollments = pd.read_csv("DataSets/Enrollments.csv", sep=";")
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#enrollments = np.array(enrollments["Enrollments"])
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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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#sunspots = pd.read_csv("DataSets/sunspots.csv", sep=",")
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#sunspots = np.array(sunspots["SUNACTIVITY"])
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#gauss = random.normal(0,1.0,5000)
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#gauss_teste = random.normal(0,1.0,400)
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#taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
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#taiex = np.array(taiexpd["avg"][:5000])
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#del(taiexpd)
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#nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
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#nasdaq = np.array(nasdaqpd["avg"][0:5000])
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#del(nasdaqpd)
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#sp500pd = pd.read_csv("DataSets/S&P500.csv", sep=",")
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#sp500 = np.array(sp500pd["Avg"][11000:])
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#del(sp500pd)
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#sondapd = pd.read_csv("DataSets/SONDA_BSB_HOURLY_AVG.csv", sep=";")
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#sondapd = sondapd.dropna(axis=0, how='any')
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#sonda = np.array(sondapd["glo_avg"])
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#del(sondapd)
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#bestpd = pd.read_csv("DataSets/BEST_TAVG.csv", sep=";")
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#best = np.array(bestpd["Anomaly"])
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#del(bestpd)
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#print(lag)
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#print(a)
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from pyFTS.benchmarks import benchmarks as bchmk
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#from pyFTS.benchmarks import distributed_benchmarks as bchmk
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#from pyFTS.benchmarks import parallel_benchmarks as bchmk
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from pyFTS.benchmarks import Util
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from pyFTS.benchmarks import arima, quantreg, Measures
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#Util.cast_dataframe_to_synthetic_point("experiments/taiex_point_analitic.csv","experiments/taiex_point_sintetic.csv",11)
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#Util.plot_dataframe_point("experiments/taiex_point_sintetic.csv","experiments/taiex_point_analitic.csv",11)
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"""
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arima100 = arima.ARIMA("", alpha=0.25)
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#tmp.appendTransformation(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.appendTransformation(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.appendTransformation(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.appendTransformation(diff)
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arima201.train(passengers, None, order=(2,0,1))
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#tmp = quantreg.QuantileRegression("", alpha=0.25, dist=True)
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#tmp.appendTransformation(diff)
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#tmp.train(sunspots[:150], None, order=1)
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#teste = tmp.forecastAheadInterval(sunspots[150:155], 5)
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#teste = tmp.forecastAheadDistribution(nasdaq[1600:1604], steps=5, resolution=50)
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bchmk.plot_compared_series(enrollments,[tmp], ['blue','red'], points=False, intervals=True)
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#print(sunspots[150:155])
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#print(teste)
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#kk = Measures.get_interval_statistics(nasdaq[1600:1605], tmp)
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#print(kk)
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"""
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"""
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bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4,#models=[yu.WeightedFTS], # #
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partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
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partitions= np.arange(10,200,step=10), #transformation=diff,
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dump=True, save=True, file="experiments/sondaws_point_analytic.csv",
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nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
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bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4, #models=[yu.WeightedFTS], # #
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partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
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partitions= np.arange(3,20,step=2), #transformation=diff,
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dump=True, save=True, file="experiments/sondaws_point_analytic_diff.csv",
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nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
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bchmk.interval_sliding_window(best, 5000, train=0.8, inc=0.8,#models=[yu.WeightedFTS], # #
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partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
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partitions= np.arange(10,200,step=10),
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dump=True, save=True, file="experiments/best"
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"_interval_analytic.csv",
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nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
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bchmk.interval_sliding_window(taiex, 2000, train=0.8, inc=0.1, #models=[yu.WeightedFTS], # #
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partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
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partitions= np.arange(3,20,step=2), transformation=diff,
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dump=True, save=True, file="experiments/taiex_interval_analytic_diff.csv",
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nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
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bchmk.ahead_sliding_window(sonda, 10000, steps=10, resolution=10, train=0.2, inc=0.2,
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partitioners=[Grid.GridPartitioner],
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partitions= np.arange(10,200,step=10), indexer=ix,
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dump=True, save=True, file="experiments/sondawind_ahead_analytic.csv",
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nodes=['192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
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bchmk.ahead_sliding_window(sonda, 10000, steps=10, resolution=10, train=0.2, inc=0.2,
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partitioners=[Grid.GridPartitioner],
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partitions= np.arange(3,20,step=2), transformation=diff, indexer=ix,
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dump=True, save=True, file="experiments/sondawind_ahead_analytic_diff.csv",
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nodes=['192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
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"""
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from pyFTS import pwfts
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from pyFTS.common import Transformations
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from pyFTS.partitioners import Grid
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#diff = Transformations.Differential(1)
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#fs = Grid.GridPartitioner(best, 190) #, transformation=diff)
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#model = pwfts.ProbabilisticWeightedFTS("FTS 1")
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#model.appendTransformation(diff)
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#model.train(best[0:1600],fs.sets, order=3)
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#bchmk.plot_compared_intervals_ahead(best[1600:1700],[model], ['blue','red'],
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# distributions=[True], save=True, file="pictures/best_ahead_forecasts",
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# time_from=40, time_to=60, resolution=100)
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'''
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experiments = [
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["experiments/taiex_point_synthetic_diff.csv","experiments/taiex_point_analytic_diff.csv",16],
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["experiments/nasdaq_point_synthetic_diff.csv","experiments/nasdaq_point_analytic_diff.csv", 11],
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["experiments/sp500_point_synthetic_diff.csv","experiments/sp500_point_analytic_diff.csv", 21],
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["experiments/best_point_synthetic_diff.csv","experiments/best_point_analytic_diff.csv", 13],
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["experiments/sondasun_point_synthetic_diff.csv","experiments/sondasun_point_analytic_diff.csv", 15],
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["experiments/sondawind_point_synthetic_diff.csv","experiments/sondawind_point_analytic_diff.csv", 8],
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["experiments/gauss_point_synthetic_diff.csv","experiments/gauss_point_analytic_diff.csv", 16]
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]
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Util.unified_scaled_point(experiments,tam=[15,8],save=True,file="pictures/unified_experiments_point.png",
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ignore=['ARIMA(1,0,0)','ARIMA(2,0,0)','ARIMA(2,0,1)','ARIMA(2,0,2)','QAR(2)'],
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replace=[['ARIMA','ARIMA'],['QAR','QAR']])
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'''
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'''
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experiments = [
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["experiments/taiex_interval_synthetic.csv","experiments/taiex_interval_analytic.csv",16],
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["experiments/nasdaq_interval_synthetic_diff.csv","experiments/nasdaq_interval_analytic_diff.csv",11],
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["experiments/sp500_interval_synthetic_diff.csv","experiments/sp500_interval_analytic_diff.csv", 11],
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["experiments/best_interval_synthetic_diff.csv","experiments/best_interval_analytic_diff.csv",13],
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["experiments/sondasun_interval_synthetic_diff.csv","experiments/sondasun_interval_analytic_diff.csv",8],
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["experiments/sondawind_interval_synthetic_diff.csv","experiments/sondawind_interval_analytic_diff.csv",8],
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["experiments/gauss_interval_synthetic_diff.csv","experiments/gauss_interval_analytic_diff.csv", 8]
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]
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Util.unified_scaled_interval(experiments,tam=[15,8],save=True,file="pictures/unified_experiments_interval.png",
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ignore=['ARIMA(1,0,0)', 'ARIMA(2,0,0)', 'ARIMA(2,0,1)', 'ARIMA(2,0,2)', 'QAR(2)'],
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replace=[['ARIMA(1,0,1) - 0.05', 'ARIMA 0.05'], ['ARIMA(1,0,1) - 0.25', 'ARIMA 0.25'],
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['QAR(1) - 0.05', 'QAR 0.05'], ['QAR(1) - 0.25', 'QAR 0.25']])
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Util.unified_scaled_interval_pinball(experiments,tam=[15,8],save=True,file="pictures/unified_experiments_interval_pinball.png",
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ignore=['ARIMA(1,0,0)', 'ARIMA(2,0,0)', 'ARIMA(2,0,1)', 'ARIMA(2,0,2)', 'QAR(2)'],
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replace=[['ARIMA(1,0,1) - 0.05', 'ARIMA 0.05'], ['ARIMA(1,0,1) - 0.25', 'ARIMA 0.25'],
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['QAR(1) - 0.05', 'QAR 0.05'], ['QAR(1) - 0.25', 'QAR 0.25']])
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'''
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experiments = [
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["experiments/taiex_ahead_synthetic.csv","experiments/taiex_ahead_analytic.csv",16],
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["experiments/nasdaq_ahead_synthetic.csv","experiments/nasdaq_ahead_analytic.csv",11],
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["experiments/sp500_ahead_synthetic.csv","experiments/sp500_ahead_analytic.csv", 21],
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["experiments/best_ahead_synthetic.csv","experiments/best_ahead_analytic.csv", 24],
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["experiments/sondasun_ahead_synthetic.csv","experiments/sondasun_ahead_analytic.csv",13],
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["experiments/sondawind_ahead_synthetic.csv","experiments/sondawind_ahead_analytic.csv", 13],
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["experiments/gauss_ahead_synthetic_diff.csv","experiments/gauss_ahead_analytic_diff.csv",16]
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]
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Util.unified_scaled_ahead(experiments,tam=[15,8],save=True,file="pictures/unified_experiments_ahead.png",
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ignore=['ARIMA(1,0,0)', 'ARIMA(0,0,1)', 'ARIMA(2,0,0)', 'ARIMA(2,0,1)',
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'ARIMA(2,0,2)', 'QAR(2)', 'ARIMA0.05'],
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replace=[['ARIMA(1,0,1) - 0.05', 'ARIMA 0.05'], ['ARIMA(1,0,1) - 0.25', 'ARIMA 0.25'],
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['QAR(1) - 0.05', 'QAR 0.05'], ['QAR(1) - 0.25', 'QAR 0.25']])
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"""
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from pyFTS.partitioners import Grid
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from pyFTS import sfts
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#print(ix.get_season_of_data(best[:2000]))
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#print(ix.get_season_by_index(45))
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#ix = SeasonalIndexer.LinearSeasonalIndexer([720,24],[False,True,False])
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#print(ix.get_season_of_data(sonda[6500:9000])[-20:])
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diff = Transformations.Differential(1)
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fs = Grid.GridPartitioner(sonda[:9000], 10, transformation=diff)
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tmp = sfts.SeasonalFTS("")
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tmp.indexer = ix
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tmp.appendTransformation(diff)
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#tmp = pwfts.ProbabilisticWeightedFTS("")
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#tmp.appendTransformation(diff)
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tmp.train(sonda[:9000], fs.sets, order=1)
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x = tmp.forecast(sonda[:1610])
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#print(taiex[1600:1610])
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print(x)
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#""" |