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Python uses ARIMA and arimax to predict the time series data of store commodity sales demand

2022-07-25 12:59:35【51CTO】

This paper explores different time series techniques on relatively simple data sets .

Given 5 Store sales data for , And ask you to predict 10 From different stores 50 Different products in 3 Sales within months .

What is the best way to deal with seasonality ？ The store should be modeled separately , They can still be combined ？

Store project demand forecast

Autoregressive composite moving average (ARIMA)

this ARIMA The model can be applied to nonstationary time series ARMA Generalization of the model .

       
       import time
       
import pandas as pd
       
%matplotlib inline
      
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Load data

       
       d_trn = pd.rad_csv('../inuraicsv, prse_tes=date'], inx_col['te'])
       
d_ts = pd.ra_csv'../iputst.csv', prse_des=['date'], ine_col['d
      
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Python use ARIMA ,ARIMAX Time series data for predicting store commodity sales demand _r Language

Python use ARIMA ,ARIMAX Time series data for predicting store commodity sales demand _ The time series _02

Python use ARIMA ,ARIMAX Time series data for predicting store commodity sales demand _ The time series _03

All stores seem to show the same trend and seasonality .

ARIMAX

Autoregressive composite moving average with explanatory variables (ARIMAX) yes ARIMA Extended version of , These include independent predictors .

Prepare the data

       
       mnths = df_rinindx.nth
       
df_ran.drpna(iplac=True)
       
d_trin.head()
      
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Python use ARIMA ,ARIMAX Time series data for predicting store commodity sales demand _ The time series _04

       
       import datetime
       
dumymns = pd.get_dummies(moth)
       
prev_uate_dates = d_tet_x.index - datie.timedelta(das=91)
       
dfetex.head()
      
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Python use ARIMA ,ARIMAX Time series data for predicting store commodity sales demand _r Language _05

Build the model

       
       si1 = d_rin.loc[(d_tin['store'] == 1) & (_tran['ie'] == 1), 'ses']
       
exog_s1i1 = df_train.loc[(df_train['store'] == 1) & (df_train['item'] == 
       
ax = SARIMAX(si1.loc['2013-12-31':], exog=exog
       
                                   nfoceinvetiblity=alse,enforce_ationarity=False, 
      
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Python use ARIMA ,ARIMAX Time series data for predicting store commodity sales demand _ The time series _06

Make predictions

       
        nog = df_rai.loc[(ftrin['str'] == s) & (df_rin['te'] == i), 'als']
       
        SARIMAX(endog=edog exog=xo,
       
                                           enorce_invtiilit=False, eorce_statnarityFalse, freq='D',
       
                                           order=(7,0,0)).fit()
       
        tc = time.time()
      
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Python use ARIMA ,ARIMAX Time series data for predicting store commodity sales demand _pandas_07

Python use ARIMA ,ARIMAX Time series data for predicting store commodity sales demand _ development language _08

Python use ARIMA ,ARIMAX Time series data for predicting store commodity sales demand _ development language _09

Example forecast

       
       xg = f_rin.loc[(df_rin[ste'] == 10) & (d_tri['itm'] == 50)].drop(['', 'ite', 'sas'], axis=1)
       
forast = arax.predict
      
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Python use ARIMA ,ARIMAX Time series data for predicting store commodity sales demand _pandas_10

Python use ARIMA ,ARIMAX Time series data for predicting store commodity sales demand _r Language _11

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