Multivariate forecasting on cmvfts
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Petrônio Cândido
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@ -88,7 +88,7 @@ class FTS(object):
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:param data: time series with minimal length to the order of the model
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:keyword type: the forecasting type, one of these values: point(default), interval or distribution.
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:keyword type: the forecasting type, one of these values: point(default), interval, distribution or multivariate.
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:keyword steps_ahead: The forecasting horizon, i. e., the number of steps ahead to forecast
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:keyword start: in the multi step forecasting, the index of the data where to start forecasting
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:keyword distributed: boolean, indicate if the forecasting procedure will be distributed in a dispy cluster
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@ -130,6 +130,8 @@ class FTS(object):
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ret = self.forecast_interval(ndata, **kwargs)
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elif type == 'distribution':
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ret = self.forecast_distribution(ndata, **kwargs)
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elif type == 'multivariate':
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ret = self.forecast_multivariate(ndata, **kwargs)
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elif steps_ahead > 1:
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if type == 'point':
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ret = self.forecast_ahead(ndata, steps_ahead, **kwargs)
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@ -137,8 +139,10 @@ class FTS(object):
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ret = self.forecast_ahead_interval(ndata, steps_ahead, **kwargs)
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elif type == 'distribution':
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ret = self.forecast_ahead_distribution(ndata, steps_ahead, **kwargs)
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elif type == 'multivariate':
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ret = self.forecast_ahead_multivariate(ndata, **kwargs)
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if not ['point', 'interval', 'distribution'].__contains__(type):
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if not ['point', 'interval', 'distribution', 'multivariate'].__contains__(type):
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raise ValueError('The argument \'type\' has an unknown value.')
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else:
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@ -184,6 +188,16 @@ class FTS(object):
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"""
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raise NotImplementedError('This model do not perform one step ahead distribution forecasts!')
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def forecast_multivariate(self, data, **kwargs):
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"""
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Multivariate forecast one step ahead
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:param data: Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model
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:param kwargs: model specific parameters
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:return: a Pandas Dataframe object representing the forecasted values for each variable
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"""
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raise NotImplementedError('This model do not perform one step ahead multivariate forecasts!')
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def forecast_ahead(self, data, steps, **kwargs):
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"""
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Point forecast n steps ahead
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@ -233,6 +247,17 @@ class FTS(object):
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"""
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raise NotImplementedError('This model do not perform multi step ahead distribution forecasts!')
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def forecast_ahead_multivariate(self, data, steps, **kwargs):
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"""
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Multivariate forecast n step ahead
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:param data: Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model
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:param steps: the number of steps ahead to forecast
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:param kwargs: model specific parameters
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:return: a Pandas Dataframe object representing the forecasted values for each variable
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"""
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raise NotImplementedError('This model do not perform one step ahead multivariate forecasts!')
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def train(self, data, **kwargs):
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"""
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Method specific parameter fitting
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@ -1,5 +1,6 @@
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import numpy as np
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import pandas as pd
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from pyFTS.common import FuzzySet, FLR, fts, flrg
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from pyFTS.models import hofts
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from pyFTS.models.multivariate import mvfts, grid, common
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@ -55,24 +56,38 @@ class ClusteredMVFTS(mvfts.MVFTS):
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if self.model.is_high_order:
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self.model.order = self.order
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if self.pre_fuzzyfy:
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ndata = self.fuzzyfy(data)
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else:
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ndata = [self.format_data(k) for k in data.to_dict('records')]
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ndata = self.check_data(data)
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self.model.train(ndata, fuzzyfied=self.pre_fuzzyfy)
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self.cluster.prune()
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def check_data(self, data):
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if self.pre_fuzzyfy:
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ndata = self.fuzzyfy(data)
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else:
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ndata = [self.format_data(k) for k in data.to_dict('records')]
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return ndata
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def forecast(self, ndata, **kwargs):
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if self.pre_fuzzyfy:
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ndata = self.fuzzyfy(ndata)
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else:
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ndata = [self.format_data(k) for k in ndata.to_dict('records')]
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ndata = self.check_data(ndata)
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return self.model.forecast(ndata, fuzzyfied=self.pre_fuzzyfy, **kwargs)
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def forecast_multivariate(self, data, **kwargs):
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ndata = self.check_data(data)
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ret = {}
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for var in self.explanatory_variables:
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self.cluster.change_target_variable(var)
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ret[var.name] = self.model.forecast(ndata, fuzzyfied=self.pre_fuzzyfy, **kwargs)
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columns = ret.keys()
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return pd.DataFrame(ret, columns=columns)
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def __str__(self):
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"""String representation of the model"""
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return str(self.model)
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@ -86,7 +86,6 @@ class GridCluster(partitioner.Partitioner):
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sys.setrecursionlimit(1000)
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def knn(self, data):
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tmp = [data[k.name]
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for k in self.mvfts.explanatory_variables]
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@ -106,3 +105,7 @@ class GridCluster(partitioner.Partitioner):
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def fuzzyfy(self, data, **kwargs):
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return fuzzyfy_instance_clustered(data, self, **kwargs)
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def change_target_variable(self, variable):
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for fset in self.sets:
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self.sets[fset].target_variable = variable
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@ -18,184 +18,21 @@ from pyFTS.models.multivariate import common, variable, mvfts, cmvfts
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from pyFTS.models.seasonal import partitioner as seasonal
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from pyFTS.models.seasonal.common import DateTime
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dataset = pd.read_csv('https://query.data.world/s/2bgegjggydd3venttp3zlosh3wpjqj', sep=';')
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from pyFTS.data import henon
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df = henon.get_dataframe(iterations=1000)
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dataset['data'] = pd.to_datetime(dataset["data"], format='%Y-%m-%d %H:%M:%S')
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from pyFTS.models.multivariate import variable, cmvfts
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data = dataset['glo_avg'].values
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train_mv = dataset.iloc[:24505]
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test_mv = dataset.iloc[24505:]
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'''
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model = Util.load_obj('/home/petronio/Downloads/ClusteredMVFTS1solarorder2knn3')
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data = [[12, 100], [13, 200]]
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for k in data:
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k[0] = pd.to_datetime('2018-01-01 {}:00:00'.format(k[0]), format='%Y-%m-%d %H:%M:%S')
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df = pd.DataFrame(data, columns=['data', 'glo_avg'])
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#forecasts = model.predict(df, steps_ahead=24, generators={'Hour': lambda x: x + pd.to_timedelta(1, unit='h')})
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#print(forecasts)
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f = lambda x: x + pd.to_timedelta(1, unit='h')
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for ix, row in df.iterrows():
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print(row['data'])
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print(f(row['data']))
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'''
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# Multivariate time series
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'''
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dataset = pd.read_csv('https://query.data.world/s/2bgegjggydd3venttp3zlosh3wpjqj', sep=';')
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dataset['data'] = pd.to_datetime(dataset["data"], format='%Y-%m-%d %H:%M:%S')
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train_mv = dataset.iloc[:24505]
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test_mv = dataset.iloc[24505:]
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sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k) for k in range(0,24)]}
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vhour = variable.Variable("Hour", data_label="data", partitioner=seasonal.TimeGridPartitioner, npart=24,
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data=train_mv, partitioner_specific=sp)
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vavg = variable.Variable("Radiation", data_label="glo_avg", alias='rad',
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partitioner=Grid.GridPartitioner, npart=30, alpha_cut=.3,
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data=train_mv)
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vx = variable.Variable("x", data_label="x", partitioner=Grid.GridPartitioner, npart=15, data=df)
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vy = variable.Variable("y", data_label="y", partitioner=Grid.GridPartitioner, npart=15, data=df)
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model = cmvfts.ClusteredMVFTS(pre_fuzzyfy=False, knn=3, fts_method=pwfts.ProbabilisticWeightedFTS)
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model.append_variable(vhour)
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model.append_variable(vavg)
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model.target_variable = vavg
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model.fit(train_mv)
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model.append_variable(vx)
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model.append_variable(vy)
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model.target_variable = vx
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Util.persist_obj(model, model.shortname)
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'''
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model.fit(df.iloc[:800])
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#model = Util.load_obj("ClusteredMVFTS")
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df = model.predict(df.iloc[800:], type='multivariate')
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model = Util.load_obj("ClusteredMVFTS2loadorder2knn2")
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print(model)
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print(model.predict(test_mv))
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'''
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train_mv = {}
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test_mv = {}
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models = {}
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for key in ['price', 'solar', 'load']:
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models[key] = []
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dataset = pd.read_csv('/home/petronio/Downloads/priceHong')
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dataset['hour'] = dataset.index.values % 24
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data = dataset['price'].values.flatten()
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train_split = 24 * 800
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train_mv['price'] = dataset.iloc[:train_split]
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test_mv['price'] = dataset.iloc[train_split:]
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dataset = pd.read_csv('https://query.data.world/s/2bgegjggydd3venttp3zlosh3wpjqj', sep=';')
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dataset['data'] = pd.to_datetime(dataset["data"], format='%Y-%m-%d %H:%M:%S')
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train_mv['solar'] = dataset.iloc[:24505]
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test_mv['solar'] = dataset.iloc[24505:]
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from pyFTS.data import Malaysia
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dataset = Malaysia.get_dataframe()
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dataset["time"] = pd.to_datetime(dataset["time"], format='%m/%d/%y %I:%M %p')
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train_mv['load'] = dataset.iloc[:train_split]
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test_mv['load'] = dataset.iloc[train_split:]
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exogenous = {}
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endogenous = {}
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for key in models.keys():
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exogenous[key] = {}
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vhour = variable.Variable("Hour", data_label="hour", partitioner=seasonal.TimeGridPartitioner, npart=24,
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data=train_mv['price'],
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partitioner_specific={'seasonality': DateTime.hour_of_day, 'type': 'common'})
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exogenous['price']['Hour'] = vhour
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vprice = variable.Variable("Price", data_label="price", partitioner=Grid.GridPartitioner, npart=55,
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data=train_mv['price'])
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endogenous['price'] = vprice
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sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k) for k in range(0,24)]}
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vhour = variable.Variable("Hour", data_label="data", partitioner=seasonal.TimeGridPartitioner, npart=24,
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data=train_mv['solar'], partitioner_specific=sp)
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exogenous['solar']['Hour'] = vhour
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vavg = variable.Variable("Radiation", data_label="glo_avg", alias='rad',
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partitioner=Grid.GridPartitioner, npart=30, alpha_cut=.3,
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data=train_mv['solar'])
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endogenous['solar'] = vavg
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sp = {'seasonality': DateTime.hour_of_day}
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vhourp = variable.Variable("Hour", data_label="time", partitioner=seasonal.TimeGridPartitioner, npart=24,
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data=train_mv['load'], partitioner_specific=sp)
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exogenous['load']['Hour'] = vhourp
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vload = variable.Variable("load", data_label="load", partitioner=Grid.GridPartitioner, npart=10,
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data=train_mv['load'])
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endogenous['load'] = vload
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from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts
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fig, ax = plt.subplots(nrows=3, ncols=1, figsize=[15,15])
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parameters = [
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{},{},
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{'order':2, 'knn': 1},
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{'order':2, 'knn': 2},
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{'order':2, 'knn': 3},
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]
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for ct, key in enumerate(models.keys()):
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for ct2, method in enumerate([mvfts.MVFTS, wmvfts.WeightedMVFTS,
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cmvfts.ClusteredMVFTS,cmvfts.ClusteredMVFTS,cmvfts.ClusteredMVFTS]):
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print(key, method, parameters[ct2])
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model = method(**parameters[ct2])
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_key2 = ""
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for k in parameters[ct2].keys():
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_key2 += k + str(parameters[ct2][k])
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model.shortname += str(ct) + key + _key2
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for var in exogenous[key].values():
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model.append_variable(var)
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model.append_variable(endogenous[key])
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model.target_variable = endogenous[key]
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model.fit(train_mv[key])
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models[key].append(model.shortname)
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Util.persist_obj(model, model.shortname)
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del(model)
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'''
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print(df)
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