Question

我想在auto.arima()中实现R python函数的等价物。

在R auto.arima函数中，时间序列值作为输入计算ARIMA顺序参数（p，d，q值）并适合模型，不需要提供p，d，q值作为用户的输入。

我想在python中使用等效的auto.arima函数（不调用auto.arima R）来预测时间序列中的未来值。在以下时间序列中执行auto.arima-python 40个点并预测接下来的6个值，然后将窗口移动1个点并再次执行相同的过程。

以下是示例性数据：

我尝试使用AD Fuller Test编写函数来计算差分顺序，通过差分时间序列（根据adfuller测试结果将原始时间序列差分后变为静止）到arma顺序选择函数来计算P，Q顺序值。

进一步使用这些值传递给Statsmodels中的arima函数。但这些功能似乎不起作用。

import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.stattools import acf, pacf

def diff_terms(timeseries):
    i=1
    j=0
    while i != 0:
        dftest = adfuller(timeseries, autolag='AIC')
        if dftest[0] <= dftest[4]["5%"]:
            i = 0
        else:
            timeseries = np.diff(timeseries)
            i = 1
            j = j + 1
    return j

def p_q_values_estimator(timeseries):
    p=0
    q=0
    lag_acf = acf(timeseries, nlags=20)
    lag_pacf = pacf(timeseries, nlags=20, method='ols')
    y=1.96/np.sqrt(len(timeseries))

    if lag_acf[0] < y:
        for a in lag_acf:
            if a < y:
                q = q + 1
                break 
    elif lag_acf[0] > y:
        for c in lag_acf:
            if c > y:
                q = q + 1
                break

    if lag_pacf[0] < y:
        for b in lag_pacf:
            if b < y:
                p = p + 1
                break
    elif lag_pacf[0] > y:
        for d in lag_pacf:
            if d > y:
                p = p + 1
                break

    p_q=[p,q]
    return(p_q)

def p_q_values_estimator2(timeseries):
    res = sm.tsa.arma_order_select_ic(timeseries, ic=['aic'], max_ar=5, max_ma=4,trend='nc')
    return res.aic_min_order

data1=[]
data=pd.read_csv('ABC.csv')
d_value=diff_terms(data.value)
data1[:]=data[:]
data = data[0:40]

i=0
while i < d_value:
    data_diff = np.diff(data)
    i = i+1

p_q_values=p_q_values_estimator(data)
p_value=p_q_values[0]
q_value=p_q_values[1]

p_q_values2=p_q_values_estimator2(data_diff)
p_value2=p_q_values2[0]
q_value2=p_q_values2[1]


exogx = np.array(range(0,40))
fit2 = sm.tsa.ARIMA(np.array(data), (p_value, d_value, q_value), exog = exogx).fit()
print(fit2.fittedvalues)
pred2 = fit2.predict(start = 40, end = 45, exog = np.array(range(40,46)))
print(pred2)
plt.plot(fit2.fittedvalues)
plt.plot(np.array(data))
plt.plot(range(40,45), np.array(pred2))
plt.show()

错误 - 使用arma order select

p_q_values2=p_q_values_estimator2(data_diff)
line 56, in p_q_values_estimator2
res = sm.tsa.arma_order_select_ic(timeseries, ic=['aic'], max_ar=5, max_ma=4,trend='nc')
File "C:\Python27\lib\site-packages\statsmodels\tsa\stattools.py", line 1052, in arma_order_select_ic min_res.update({i + '_min_order' : (mins[0][0], mins[1][0])})
IndexError: index 0 is out of bounds for axis 0 with size 0

错误 - 使用基于acf pacf的函数计算P，Q顺序

fit2 = sm.tsa.ARIMA(np.array(data), (p_value, d_value, q_value), exog = exogx).fit()
File "C:\Python27\lib\site-packages\statsmodels\tsa\arima_model.py", line 1104, in fit
callback, **kwargs)
File "C:\Python27\lib\site-packages\statsmodels\tsa\arima_model.py", line 942, in fit
armafit.mle_retvals = mlefit.mle_retvals
AttributeError: 'LikelihoodModelResults' object has no attribute 'mle_retvals'

Answer 1

Vals是我自己的事情，但您可以使用pd.date_range

创建自己的索引

rdata=ts(traindf.requests_per_active.values,frequency=12)
#forecasts
fit=forecast.auto_arima(rdata)
forecast_output=forecast.forecast(fit,h=6,level=(95.0))
#convert forecasts to dataframe     
forecast_results=pd.Series(forecast_output[3], index=vals.index)
lowerpi=pd.Series(forecast_output[4], index=vals.index)
upperpi=pd.Series(forecast_output[5], index=vals.index)
results = pd.DataFrame({'forecast' : forecast_results, 'lowerpi' : lowerpi, 'upperpi' : upperpi})

R的auto.arima（）等效于Python

1 个答案: