python-arima模型statsmodels库实现-有数据集

python-arima模型statsmodels库实现-有数据集

最近，帮同学做一些任务，碰到了需要用到arima模型，我就把自己的实现代码给大家学习一下，当然也有数据集，可以帮助大家测试代码，有问题的话，可以咨询我

数据集，我直接上传到，我的资源中了，名为arima模型学习数据集
下面arima模型实现代码，有参数选择，也有绘图，也有模型预测，还有数据差分和平稳性检验

import  numpy  as np
import pandas as pd
import os
from numpy import NaN
from numpy import nan
import matplotlib.pyplot as plt
import statsmodels.api as sm     #acf,pacf图
from statsmodels.tsa.stattools import adfuller  #adf检验
from pandas.plotting import autocorrelation_plot
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.stats.diagnostic import acorr_ljungbox

import statsmodels.api as sm
import matplotlib as mpl
path="C:/Users/gaoxi/Desktop/china_data.xlsx"
# 为了控制计算量，我们限制AR最大阶不超过6，MA最大阶不超过4。





plt.style.use('fivethirtyeight')
mpl.rcParams['font.sans-serif'] = ['SimHei']
mpl.rcParams['font.serif'] = ['SimHei']
mpl.rcParams['axes.unicode_minus'] = False
df=pd.read_excel(path)
#print(df)
#help(df)

#for index, row in df.iterrows():

df=df.replace(NaN, "null")
#  print(index, row)
print(df)
def  f(column):
    r=0
    inde1=0
    index2=len(column)-1
    for i in range(len(column)):
     #   print(column[len(column)-i-1])
        if   column[len(column)-i-1] is "null" and r==1:
            index2=i
            return index1,index2
       
        if   column[len(column)-i-1]!= "null" and r==0:
             index1=i
             r=1
    return index1,index2
           
        
#df['时间(年)']=pd.to_datetime(df['时间(年)'])

print(df.columns)
print(df[df.columns[0]])
indexz=df.columns[0]


def adf_test(data):#小于0.05则是平稳序列
    t = adfuller(data)
    print("p-value:",t[1])
def  box_pierce_test(data):#小于0.05，不是白噪声序列
    print(acorr_ljungbox(data, lags=1)) 

def  stability_judgment(data):
    fig = plt.figure(figsize=(12,8))
    ax1=fig.add_subplot(211)
    fig = sm.graphics.tsa.plot_acf(data,lags=5,ax=ax1)
    ax2 = fig.add_subplot(212)
    fig = sm.graphics.tsa.plot_pacf(data,lags=5,ax=ax2)
    plt.show()


def  model_fit(data,df,index,length,index1,index2):
    data_diff=df[["时间(年)",index]][length-index2:length-index1]
  #  sm.tsa.arma_order_select_ic(data_diff,max_ar=6,max_ma=4,ic='aic')['aic_min_order']  # AIC


    #对模型进行定阶
    pmax = int(len(data) / 10)    #一般阶数不超过 length /10
    qmax = int(len(data) / 10)
    if  pmax>4:pmax=6
    if  qmax>4:qmax=4
    bic_matrix = []
    print(data)

   # help(sm.tsa.arima.ARIMA)
    for p in range(pmax +1):
        temp= []
        for q in range(qmax+1):
            try:
         #  ARIMA(train_data, order=(1,1,1))

          # print(sm.tsa.arima.ARIMA(data,order=(p,1,q)).fit())
                  temp.append(sm.tsa.arima.ARIMA(data,order=(p,1,q)).fit().bic)
                #  print(temp)
            except:
                temp.append(None)
            bic_matrix.append(temp)
             
    bic_matrix = pd.DataFrame(bic_matrix)   #将其转换成Dataframe 数据结构
    print(bic_matrix)
    p,q = bic_matrix.stack().astype(float).idxmin()   #先使用stack 展平， 然后使用 idxmin 找出最小值的位置
    print(u'BIC 最小的p值 和 q 值：%s,%s' %(p,q))  #  BIC 最小的p值 和 q 值：0,1
   
    model = sm.tsa.arima.ARIMA(data, order=(p,1,q)).fit()
    model.summary()        #生成一份模型报告
    predictions_ARIMA_diff = pd.Series(model.fittedvalues, copy=True)
    print(predictions_ARIMA_diff)
    model.forecast(5)   #为未来5天进行预测， 返回预测结果， 标准误差， 和置信区间


    


for index, column in df.iteritems():
    if index==indexz:
        continue
    index1,index2 =f(column)
    length=len(column)
   # print("index1 index2：",index1,index2)

  #  print(column[length-index2-1:length-index1])
    print(index)
    df[index]=df[index].replace( "null",0)
    df[index].astype('float')
    
    df[str(index)+"diff1"]=df[index].diff(1)
    df[str(index)+"diff2"]=df[index+"diff1"].diff(1)
    # 一阶差分还原
    # tmpdata2：原数据
    # pred：一阶差分后的预测数据
    #df_shift = tmpdata2['ecpm_tomorrow'].shift(1)
    #predict = pred.add(df_shift)
    # predict = pred + df_shift

   # print(index2-index1)
    #print(df[["时间(年)",index]][length-index2:length-index1])
    adf_test(df[[index]][length-index2:length-index1])
    box_pierce_test(df[[index]][length-index2:length-index1])
    model_fit(df[[index]][length-index2:length-index1],df,index,length,index1,index2)
   
  ## model_fit(data,p,q)


    stability_judgment(df[[index]][length-index2:length-index1])

    stability_judgment(df[[str(index)+"diff1"]][length-index2:length-index1])
  #  stability_judgment(df[[str(index)+"diff2"]][length-index2:length-index1])

    plt.plot(df[["时间(年)"]][length-index2:length-index1],df[[index]][length-index2:length-index1],label="diff0")
    plt.plot(df[["时间(年)"]][length-index2:length-index1],df[[str(index)+"diff1"]][length-index2:length-index1],label="diff1")
 #   plt.plot(df[["时间(年)"]][length-index2:length-index1],df[[str(index)+"diff2"]][length-index2:length-index1],label="diff2")

   # df[["时间(年)",index]][length-index2:length-index1].plot(x=indexz,y=index,figsize=(9,9))
    plt.xlabel("时间（年）")
    plt.ylabel(index)
    plt.legend()
    plt.show()
 




os.system("pause")