54 lines
1.8 KiB
Python
54 lines
1.8 KiB
Python
#!/usr/bin/python
|
|
# -*- coding: utf8 -*-
|
|
|
|
import numpy as np
|
|
from statsmodels.tsa.arima_model import ARIMA as stats_arima
|
|
from pyFTS import fts
|
|
|
|
|
|
class ARIMA(fts.FTS):
|
|
def __init__(self, order, **kwargs):
|
|
super(ARIMA, self).__init__(1, "ARIMA")
|
|
self.name = "ARIMA"
|
|
self.detail = "Auto Regressive Integrated Moving Average"
|
|
self.isHighOrder = True
|
|
self.model = None
|
|
self.model_fit = None
|
|
self.trained_data = None
|
|
self.p = 1
|
|
self.d = 0
|
|
self.q = 0
|
|
self.benchmark_only = True
|
|
self.minOrder = 1
|
|
|
|
def train(self, data, sets, order=1, parameters=None):
|
|
if parameters is not None:
|
|
self.p = parameters[0]
|
|
self.d = parameters[1]
|
|
self.q = parameters[2]
|
|
self.order = max([self.p, self.d, self.q])
|
|
self.shortname = "ARIMA(" + str(self.p) + "," + str(self.d) + "," + str(self.q) + ")"
|
|
|
|
old_fit = self.model_fit
|
|
self.model = stats_arima(data, order=(self.p, self.d, self.q))
|
|
try:
|
|
self.model_fit = self.model.fit(disp=0)
|
|
except:
|
|
try:
|
|
self.model = stats_arima(data, order=(self.p, self.d, self.q))
|
|
self.model_fit = self.model.fit(disp=1)
|
|
except:
|
|
self.model_fit = old_fit
|
|
|
|
self.trained_data = data #.tolist()
|
|
|
|
def forecast(self, data, **kwargs):
|
|
if self.model_fit is None:
|
|
return np.nan
|
|
ret = []
|
|
for t in data:
|
|
output = self.model_fit.forecast()
|
|
ret.append( output[0] )
|
|
self.trained_data = np.append(self.trained_data, t) #.append(t)
|
|
self.train(self.trained_data,None,order=self.order, parameters=(self.p, self.d, self.q))
|
|
return ret |