83行代码

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 pd import matplotlib.pyplot as plt import datetime from statsmodels.tsa.api import SARIMAX datapath = '../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)