import pandas as pd import numpy as np import matplotlib.pyplot as plt from statsmodels.tsa.arima_model import ARIMA from sklearn.metrics import mean_squared_error import pymysql import time,os import re import requests import urllib from datetime import datetime # ---------连接-------------- connect = pymysql.connect(host='localhost', # 本地数据库 user='root', password='123456', port=3306, charset='utf8') #服务器名,账户,密码，数据库名称 cur = connect.cursor() print(cur) # 读取数据 try: select_sqli = "SELECT time,xiaoliang FROM sheji.sale where chexing='海豚';" cur.execute(select_sqli) data = pd.DataFrame(cur.fetchall(), columns=['time', 'xiaoliang']) except Exception as e: print("读取数据失败:", e) else: print("读取数据成功") # 转换时间格式 data['time'] = pd.to_datetime(data['time'], format='%Y-%m') print(data)这个代码继续怎么写

接下来可以对数据进行预处理和可视化分析，例如：

1. 将时间列设置为数据的索引

data = data.set_index('time')

2. 对销量数据进行差分，以便将其转换为平稳的时间序列

diff_data = data.diff().dropna()

3. 对平稳时间序列进行自相关和偏自相关分析，以确定 ARIMA 模型的参数

from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

plot_acf(diff_data)
plot_pacf(diff_data)

4. 通过交叉验证选择最佳的 ARIMA 模型

from sklearn.model_selection import TimeSeriesSplit
from itertools import product

p = range(0, 3)
d = range(0, 3)
q = range(0, 3)
params = product(p, d, q)
params_list = list(params)

tss = TimeSeriesSplit(n_splits=3)

best_model = None
best_mse = np.inf

for params in params_list:
    mse_list = []
    for train_index, test_index in tss.split(diff_data):
        train_data = diff_data.iloc[train_index]
        test_data = diff_data.iloc[test_index]
        try:
            model = ARIMA(train_data, order=params)
            model_fit = model.fit(disp=False)
            pred = model_fit.forecast(len(test_data))[0]
            mse = mean_squared_error(test_data, pred)
            mse_list.append(mse)
        except:
            continue
    if len(mse_list) > 0:
        mean_mse = np.mean(mse_list)
        if mean_mse < best_mse:
            best_mse = mean_mse
            best_model = params
            
print(best_model)

5. 使用最佳的 ARIMA 模型进行预测，并将结果可视化

model = ARIMA(diff_data, order=best_model)
model_fit = model.fit(disp=False)
pred = model_fit.forecast(12)[0]
pred_cumsum = pred.cumsum()
forecast_data = pd.Series(data.iloc[-1, 0], index=pd.date_range(start=data.index[-1], periods=12, freq='M'))
forecast_data += pred_cumsum
plt.plot(data)
plt.plot(forecast_data)