- Improvementos of interval and distribution m-step ahead forecasts for arima and quantreg
This commit is contained in:
Petrônio Cândido de Lima e Silva
parent
b3e1a70602
commit
67358fa000
@ -20,6 +20,7 @@ class ARIMA(fts.FTS):
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self.is_high_order = True
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self.has_point_forecasting = True
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self.has_interval_forecasting = True
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self.has_probability_forecasting = True
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self.model = None
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self.model_fit = None
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self.trained_data = None
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@ -44,7 +45,6 @@ class ARIMA(fts.FTS):
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try:
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self.model = stats_arima(data, order=(self.p, self.d, self.q))
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self.model_fit = self.model.fit(disp=0)
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print(np.sqrt(self.model_fit.sigma2))
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except Exception as ex:
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print(ex)
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self.model_fit = None
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@ -145,6 +145,9 @@ class ARIMA(fts.FTS):
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return ret
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def empty_grid(self, resolution):
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return self.get_empty_grid(-(self.original_max*2), self.original_max*2, resolution)
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def forecastAheadDistribution(self, data, steps, **kwargs):
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smoothing = kwargs.get("smoothing", 0.5)
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@ -158,7 +161,7 @@ class ARIMA(fts.FTS):
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resolution = kwargs.get('resolution', percentile_size)
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grid = self.get_empty_grid(self.original_min, self.original_max, resolution)
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grid = self.empty_grid(resolution)
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index = SortedCollection.SortedCollection(iterable=grid.keys())
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@ -167,7 +170,7 @@ class ARIMA(fts.FTS):
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nmeans = self.forecastAhead(ndata, steps, **kwargs)
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for k in np.arange(0, steps):
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grid = self.get_empty_grid(self.original_min, self.original_max, resolution)
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grid = self.empty_grid(resolution)
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for alpha in np.arange(0.05, 0.5, 0.05):
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tmp = []
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@ -182,6 +185,6 @@ class ARIMA(fts.FTS):
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ret.append(tmp / sum(tmp))
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grid = self.get_empty_grid(self.original_min, self.original_max, resolution)
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grid = self.empty_grid(resolution)
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df = pd.DataFrame(ret, columns=sorted(grid))
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return df
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@ -16,13 +16,19 @@ import numpy as np
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import pandas as pd
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from mpl_toolkits.mplot3d import Axes3D
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from probabilistic import ProbabilityDistribution
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from pyFTS.probabilistic import ProbabilityDistribution
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from pyFTS import song, chen, yu, ismailefendi, sadaei, hofts, pwfts, ifts, cheng, ensemble, hwang
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from pyFTS.benchmarks import Measures, naive, arima, ResidualAnalysis, quantreg
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from pyFTS.benchmarks import Util as bUtil
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from pyFTS.common import Transformations, Util
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# from sklearn.cross_validation import KFold
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from pyFTS.partitioners import Grid
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from matplotlib import rc
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rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
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## for Palatino and other serif fonts use:
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#rc('font',**{'family':'serif','serif':['Palatino']})
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rc('text', usetex=True)
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colors = ['grey', 'rosybrown', 'maroon', 'red','orange', 'yellow', 'olive', 'green',
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'cyan', 'blue', 'darkblue', 'purple', 'darkviolet']
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@ -472,6 +478,21 @@ def plot_distribution(dist):
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def plot_compared_series(original, models, colors, typeonlegend=False, save=False, file=None, tam=[20, 5],
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points=True, intervals=True, linewidth=1.5):
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"""
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Plot the forecasts of several one step ahead models, by point or by interval
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:param original: Original time series data (list)
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:param models: List of models to compare
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:param colors: List of models colors
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:param typeonlegend: Add the type of forecast (point / interval) on legend
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:param save: Save the picture on file
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:param file: Filename to save the picture
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:param tam: Size of the picture
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:param points: True to plot the point forecasts, False otherwise
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:param intervals: True to plot the interval forecasts, False otherwise
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:param linewidth:
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:return:
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"""
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fig = plt.figure(figsize=tam)
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ax = fig.add_subplot(111)
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@ -504,8 +525,12 @@ def plot_compared_series(original, models, colors, typeonlegend=False, save=Fals
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upper.insert(0, None)
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lbl = fts.shortname
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if typeonlegend: lbl += " (Interval)"
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ax.plot(lower, color=colors[count], label=lbl, ls="--",linewidth=linewidth)
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ax.plot(upper, color=colors[count], ls="--",linewidth=linewidth)
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if not points and intervals:
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ls = "-"
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else:
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ls = "--"
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ax.plot(lower, color=colors[count], label=lbl, ls=ls,linewidth=linewidth)
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ax.plot(upper, color=colors[count], ls=ls,linewidth=linewidth)
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handles0, labels0 = ax.get_legend_handles_labels()
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lgd = ax.legend(handles0, labels0, loc=2, bbox_to_anchor=(1, 1))
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@ -685,7 +710,24 @@ def print_distribution_statistics(original, models, steps, resolution):
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def plot_compared_intervals_ahead(original, models, colors, distributions, time_from, time_to,
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interpol=False, save=False, file=None, tam=[20, 5], resolution=None,
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cmap='Blues',option=2):
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cmap='Blues',option=2, linewidth=1.5):
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"""
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Plot the forecasts of several one step ahead models, by point or by interval
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:param original: Original time series data (list)
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:param models: List of models to compare
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:param colors: List of models colors
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:param distributions: True to plot a distribution
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:param time_from: index of data poit to start the ahead forecasting
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:param time_to: number of steps ahead to forecast
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:param interpol: Fill space between distribution plots
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:param save: Save the picture on file
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:param file: Filename to save the picture
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:param tam: Size of the picture
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:param resolution:
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:param cmap: Color map to be used on distribution plot
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:param option: Distribution type to be passed for models
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:return:
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"""
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fig = plt.figure(figsize=tam)
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ax = fig.add_subplot(111)
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@ -739,7 +781,6 @@ def plot_compared_intervals_ahead(original, models, colors, distributions, time_
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cb.set_label('Density')
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if fts.has_interval_forecasting:
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forecasts = fts.forecastAheadInterval(original[time_from - fts.order:time_from], time_to)
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lower = [kk[0] for kk in forecasts]
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@ -749,8 +790,8 @@ def plot_compared_intervals_ahead(original, models, colors, distributions, time_
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for k in np.arange(0, time_from - fts.order):
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lower.insert(0, None)
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upper.insert(0, None)
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ax.plot(lower, color=colors[count], label=fts.shortname)
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ax.plot(upper, color=colors[count])
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ax.plot(lower, color=colors[count], label=fts.shortname, linewidth=linewidth)
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ax.plot(upper, color=colors[count], linewidth=linewidth*1.5)
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else:
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forecasts = fts.forecast(original)
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@ -760,10 +801,12 @@ def plot_compared_intervals_ahead(original, models, colors, distributions, time_
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forecasts.insert(0, None)
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ax.plot(forecasts, color=colors[count], label=fts.shortname)
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ax.plot(original, color='black', label="Original")
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ax.plot(original, color='black', label="Original", linewidth=linewidth*1.5)
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handles0, labels0 = ax.get_legend_handles_labels()
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ax.legend(handles0, labels0, loc=2)
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# ax.set_title(fts.name)
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if True in distributions:
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lgd = ax.legend(handles0, labels0, loc=2)
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else:
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lgd = ax.legend(handles0, labels0, loc=2, bbox_to_anchor=(1, 1))
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_mi = min(mi)
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if _mi < 0:
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_mi *= 1.1
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@ -780,9 +823,7 @@ def plot_compared_intervals_ahead(original, models, colors, distributions, time_
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ax.set_xlabel('T')
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ax.set_xlim([0, len(original)])
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#plt.colorbar()
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Util.showAndSaveImage(fig, file, save)
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Util.showAndSaveImage(fig, file, save, lgd=lgd)
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def plotCompared(original, forecasts, labels, title):
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@ -19,6 +19,7 @@ class QuantileRegression(fts.FTS):
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self.has_point_forecasting = True
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self.has_interval_forecasting = True
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self.has_probability_forecasting = True
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self.has_probability_forecasting = True
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self.benchmark_only = True
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self.minOrder = 1
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self.alpha = kwargs.get("alpha", 0.05)
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@ -108,14 +109,21 @@ class QuantileRegression(fts.FTS):
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def forecastAheadInterval(self, data, steps, **kwargs):
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ndata = np.array(self.doTransformations(data))
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smoothing = kwargs.get("smoothing", 0.9)
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l = len(ndata)
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ret = [[k, k] for k in ndata[-self.order:]]
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ret = []
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for k in np.arange(self.order, steps + self.order):
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intl = self.interval_to_interval([ret[x] for x in np.arange(k - self.order, k)], self.lower_qt, self.upper_qt)
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nmeans = self.forecastAhead(ndata, steps, **kwargs)
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ret.append(intl)
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for k in np.arange(0, self.order):
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nmeans.insert(k,ndata[-(k+1)])
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for k in np.arange(self.order, steps+self.order):
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intl = self.point_to_interval(nmeans[k - self.order: k], self.lower_qt, self.upper_qt)
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ret.append([intl[0]*(1 + k*smoothing), intl[1]*(1 + k*smoothing)])
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ret = self.doInverseTransformations(ret, params=[[data[-1] for a in np.arange(0, steps + self.order)]], interval=True)
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@ -4,6 +4,7 @@ from bisect import bisect_left, bisect_right
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# Original Source Code: https://code.activestate.com/recipes/577197-sortedcollection/
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# Author: RAYMOND HETTINGER
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class SortedCollection(object):
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'''Sequence sorted by a key function.
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@ -204,8 +205,8 @@ class SortedCollection(object):
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raise ValueError('No item found between keys : %r,%r' % (ge,le))
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def inside(self, ge, le):
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g = bisect_right(self._keys, ge)
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l = bisect_left(self._keys, le)
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l = bisect_right(self._keys, le)
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g = bisect_left(self._keys, ge)
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if g != len(self) and l != len(self) and g != l:
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return self._items[g : l]
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elif g != len(self) and l != len(self) and g == l:
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19
ensemble.py
19
ensemble.py
@ -6,7 +6,9 @@ import pandas as pd
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import math
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from operator import itemgetter
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from pyFTS.common import FLR, FuzzySet, SortedCollection
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from pyFTS import fts
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from pyFTS import fts, chen, cheng, hofts, hwang, ismailefendi, sadaei, song, yu
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from pyFTS.benchmarks import arima, quantreg
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from pyFTS.common import Transformations
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class EnsembleFTS(fts.FTS):
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@ -24,10 +26,14 @@ class EnsembleFTS(fts.FTS):
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def build(self, data, models, partitioners, partitions, max_order=3, transformation=None, indexer=None):
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self.models = []
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methods = [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, cheng.TrendWeightedFTS,
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ismailefendi.ImprovedWeightedFTS, sadaei.ExponentialyWeightedFTS, hwang.HighOrderFTS,
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hofts.HighOrderFTS, arima.ARIMA, quantreg.QuantileRegression]
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for count, model in enumerate(models, start=0):
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mfts = model("")
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transformations = [None, Transformations.Differential(1)]
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for count, method in enumerate(methods, start=0):
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mfts = method("")
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if mfts.benchmark_only:
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if transformation is not None:
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mfts.appendTransformation(transformation)
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@ -37,7 +43,7 @@ class EnsembleFTS(fts.FTS):
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for partition in partitions:
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for partitioner in partitioners:
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data_train_fs = partitioner(data, partition, transformation=transformation)
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mfts = model("")
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mfts = method("")
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mfts.partitioner = data_train_fs
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if not mfts.is_high_order:
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@ -50,7 +56,7 @@ class EnsembleFTS(fts.FTS):
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else:
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for order in np.arange(1, max_order + 1):
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if order >= mfts.min_order:
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mfts = model("")
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mfts = method("")
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mfts.partitioner = data_train_fs
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if transformation is not None:
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@ -60,6 +66,7 @@ class EnsembleFTS(fts.FTS):
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self.models.append(mfts)
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def train(self, data, sets, order=1,parameters=None):
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#if self.models is None:
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pass
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def forecast(self, data, **kwargs):
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@ -19,8 +19,8 @@ from numpy import random
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os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
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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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enrollments = pd.read_csv("DataSets/Enrollments.csv", sep=";")
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enrollments = np.array(enrollments["Enrollments"])
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diff = Transformations.Differential(1)
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@ -28,14 +28,20 @@ diff = Transformations.Differential(1)
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DATASETS
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"""
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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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nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
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nasdaq = np.array(nasdaqpd["avg"][0:5000])
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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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#sp500pd = pd.read_csv("DataSets/S&P500.csv", sep=",")
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#sp500 = np.array(sp500pd["Avg"][11000:])
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@ -43,7 +49,7 @@ nasdaq = np.array(nasdaqpd["avg"][0:5000])
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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["ws_10m"])
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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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@ -53,8 +59,8 @@ nasdaq = np.array(nasdaqpd["avg"][0:5000])
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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 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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@ -65,17 +71,20 @@ from pyFTS.benchmarks import arima, quantreg, Measures
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#"""
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tmp = arima.ARIMA("", alpha=0.25)
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#tmp.appendTransformation(diff)
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tmp.train(nasdaq[:1600], None, order=(1,0,1))
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teste = tmp.forecastAheadDistribution(nasdaq[1600:1604], steps=5, resolution=100)
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tmp.train(enrollments, None, order=(1,0,0))
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teste = tmp.forecastInterval(enrollments)
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#tmp = quantreg.QuantileRegression("", dist=True)
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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(nasdaq[:1600], None, order=1)
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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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print(nasdaq[1600:1605])
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print(teste)
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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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@ -109,10 +118,10 @@ bchmk.interval_sliding_window(best, 5000, train=0.8, inc=0.8,#models=[yu.Weighte
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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(best, 5000, train=0.8, inc=0.8, #models=[yu.WeightedFTS], # #
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bchmk.interval_sliding_window(sp500, 2000, train=0.8, inc=0.2, #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/best_interval_analytic_diff.csv",
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dump=True, save=True, file="experiments/sp500_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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#"""
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