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使用SARIMAX进行时间序列预测。
#!/usr/bin/env python#-*-coding:utf-8-*-#******************************************************************************#****************Description:Time Series prediction using SARIMAX#****************Author:Duan Tingyin#****************Date:2018.02.14#**************************************************import pandas as pdimport matplotlib.pyplot as pltimport datetimefrom statsmodels.tsa.api import SARIMAXdatapath = '../data/'train_df = pd.read_csv(datapath+'[new] yancheng_train_20171226.csv')testA_df = pd.read_csv(datapath+'yancheng_testA_20171225.csv')testB_df = pd.read_csv(datapath+'yancheng_testB_20180224.csv')train_class = train_df.groupby(['sale_date','class_id'])['sale_quantity'].sum().to_frame().reset_index()train_class.head()def plt_class(data,x,y,class_id): this_class_id=data[data.class_id == class_id] plt.scatter(x=this_class_id[x],y=this_class_id[y])def trans_date(x): str_x=str(x) year=int(str_x[:4]) month=int(str_x[4:]) return datetime.date(year,month,1)train_class['_sale_date']=train_class['sale_date'].apply(trans_date)testA_df['_sale_date']=testA_df['predict_date'].apply(trans_date)testB_df['_sale_date']=testB_df['predict_date'].apply(trans_date)#print(train_class.head(),testA_df.head(),testB_df.head())s="predict_date,class_id,predict_quantity"ex=[]f=open("../data/yancheng_testA_20171225.csv","r")for line in f.readlines(): if "date" in line: continue class_id=int(line.split(",")[1]) this_class_id=train_class[train_class['class_id']==class_id][['_sale_date','sale_quantity']] if class_id==653436: print(this_class_id._sale_date) #indexed_this_class_id = this_class_id.set_index(this_class_id['_sale_date']) indexed_this_class_id=this_class_id.set_index(pd.date_range(end='2017-11',periods=len(this_class_id['_sale_date']),freq='M')) print(this_class_id['_sale_date'],pd.date_range(end='2017-11',periods=len(this_class_id['_sale_date']),freq='M')) res=0 try: fit1=SARIMAX(indexed_this_class_id.sale_quantity,verbose=False).fit() pre=fit1.get_forecast().conf_int() res=(int(round((pre['lower sale_quantity'] + pre['upper sale_quantity'])*0.5))) except Exception as e: print(e) ex.append(class_id) plt_class(train_class,'sale_date','sale_quantity',class_id) res=int(this_class_id['sale_quantity'].iloc[-1]) this_class_id.to_csv('EXCEPTION'+str(class_id) +".csv",header=True,index=False,float_format='%.0f') s+="\n" s+="201711"+ ","+str(class_id) + "," +str(res)f.close()s+="\n"train_class[['sale_date','class_id','sale_quantity']].to_csv('train_class.csv',header=True,index=False,float_format='%.0f')fw=open("201711.csv","w")fw.write(s)fw.close()print(ex) 转载地址：http://wheel.baihongyu.com/