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 from statsmodels.graphics.tsaplots import plot_acf, plot_pacf # ---------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') data = data.set_index('time') diff_data = data.diff().dropna() plot_acf(diff_data) plot_pacf(diff_data) print(data)哪里有错

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 from statsmodels.graphics.tsaplots import plot_acf, plot_pacf from statsmodels.tsa.api import Holt from statsmodels.tsa.api import ExponentialSmoothing from pmdarima.arima import auto_arima #from statsmodels.tsa.stattools import pcorrelogram# 设置maxlag参数为5 # ---------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) try: select_sqli = "SELECT time FROM sheji.sale;" cur.execute(select_sqli) data = pd.DataFrame(cur.fetchall(), columns=['time']) except Exception as e: print("读取数据失败:", e) else: print("读取数据成功") data['time'] = pd.to_datetime(data['time'], format='%Y-%m') data = data.sort_index() print(data[len(data)-1])哪里有问题

这段代码的问题在于最后一行的 print(data[len(data)-1])。这里使用了 DataFrame 的行索引来获取数据，但是 DataFrame 的行索引并不是连续的数字，而是由时间序列数据组成的 DatetimeIndex。因此，使用 len(data)...

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltfrom statsmodels.tsa.arima_model import ARIMAfrom sklearn.metrics import mean_squared_errorimport pymysqlimport time, osimport reimport requestsimport urllibfrom datetime import datetimefrom statsmodels.graphics.tsaplots import plot_acf, plot_pacf# 导入模块import pandas as pdimport numpy as npimport matplotlib.pyplot as pltfrom statsmodels.tsa.arima_model import ARIMAfrom sklearn.metrics import mean_squared_errorimport pymysqlfrom datetime import datetimefrom statsmodels.graphics.tsaplots import plot_acf, plot_pacf# 连接数据库connect = pymysql.connect(host='localhost', # 本地数据库 user='root', password='123456', port=3306, charset='utf8', database='sheji') # 数据库名称cur = connect.cursor()# 读取数据try: select_sqli = "SELECT time,xiaoliang FROM 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')data = data.set_index('time')diff_data = data.diff().dropna()# 绘制自相关图和偏自相关图plot_acf(diff_data)plot_pacf(diff_data)# 确定 ARIMA 模型的参数p = 1d = 1q = 1model = ARIMA(data, order=(p, d, q))model_fit = model.fit(disp=0)# 预测销量y_pred = model_fit.predict(len(data), len(data) + 11, typ='levels')# 绘制预测结果plt.plot(data)plt.plot(y_pred, color='red')plt.show()# 关闭数据库连接cur.close()connect.close()请将这段代码改为移动平均模型

from statsmodels.tsa.arima_model import ARIMA from sklearn.metrics import mean_squared_error import pymysql from datetime import datetime from statsmodels.graphics.tsaplots import plot_acf, plot_pacf ...

import pandas as pd import matplotlib.pyplot as plt import statsmodels.api as sm import itertools import numpy as np from statsmodels.stats.diagnostic import acorr_ljungbox from statsmodels.graphics.tsaplots import plot_pacf,plot_acf from sklearn.metrics import r2_score,mean_absolute_error import seaborn as sns from scipy import stats #导入数据 ChinaBank = pd.read_csv('ChinaBank.csv',index_col = 'Date',parse_dates=['Date']) ChinaBank.head() #提取close列 sub = ChinaBank.loc['2014-01':'2014-06','Close'] print(sub.head()) #绘制时序图 plt.plot(sub) plt.show() #划分训练测试集 train = sub.loc['2014-01':'2014-05'] test = sub.loc['2014-06-01':'2014-06-30'] model = sm.tsa.arima.ARIMA(train,order=(1,1,0))#输入三个参数p d q arima_res=model.fit() arima_res.summary() #模型预测 predict=arima_res.predict('2014-06-01','2014-06-30') plt.plot(test.index,test) plt.plot(test.index,predict) plt.legend(['y_true','y_pred']) plt.show() print(len(predict)) #模型评价 print(mean_absolute_error(test,predict)) #残差分析 res=test-predict residual=list(res) plt.plot(residual) # 查看残差的均值是否在0附近 print(np.mean(residual)) # 残差正态性检验 plt.figure(figsize=(10,5)) ax=plt.subplot(1,2,1) sns.distplot(residual,fit=stats.norm) ax=plt.subplot(1,2,2) res=stats.probplot(residual,plot=plt) plt.show() 为啥预测的时候报错呢

3. 参数问题：在创建 ARIMA 模型时，参数 order=(1,1,0) 中的 p、d 和 q 的值可能不适合你的数据。你可以尝试不同的参数组合来获得更好的预测结果。 4. 模型训练问题：在进行模型训练时，可能会出现收敛问题或其他...

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 # 读取csv文件 data = pd.read_csv("77.csv", header=None) # 划分训练集和测试集 train_size = int(len(data) * 0.7) train_data, test_data = data[:train_size], data[train_size:] # 将时间编号设置为索引 train_data.set_index(0, inplace=True) test_data.set_index(0, inplace=True) # 删除原来的索引列 train_data.index.name = None test_data.index.name = None # 转换为时间序列 train_ts = train_data[1] test_ts = test_data[1] # 训练ARIMA模型 model = ARIMA(train_ts, order=(1, 1, 1)) model_fit = model.fit() # 预测测试集数据 predictions = model_fit.forecast(steps=len(test_ts))[0] # 计算rmse rmse = np.sqrt(mean_squared_error(test_ts, predictions)) print('Test RMSE: %.3f' % rmse) # 绘制图像 plt.figure(figsize=(10, 6)) plt.plot(train_ts, label='Train') plt.plot(test_ts, label='Test') plt.plot(test_ts.index, predictions, label='Predictions') plt.xlabel('Time(h)') plt.ylabel('kwh') plt.title('ARIMA Model Predictions') plt.legend() plt.show()以上代码报错如下，请解释并给出修改后的代码KeyError: 0

plt.plot(test_ts.index, predictions, label='Predictions') 修改为： python plt.plot(test_data.index, predictions, label='Predictions') 这样就可以正确绘制出预测结果的图像了。

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltfrom statsmodels.tsa.arima.model import ARIMAfrom sklearn.metrics import mean_squared_error# 读取csv文件data = pd.read_csv("data.csv", header=None)# 划分训练集和测试集train_size = int(len(data) * 0.7)train_data, test_data = data[:train_size], data[train_size:]# 将时间编号设置为索引train_data.set_index(0, inplace=True)test_data.set_index(0, inplace=True)# 转换为时间序列train_ts = train_data[1]test_ts = test_data[1]# 训练ARIMA模型model = ARIMA(train_ts, order=(1, 1, 1))model_fit = model.fit()# 预测测试集数据predictions = model_fit.forecast(steps=len(test_ts))[0]# 计算rmsermse = np.sqrt(mean_squared_error(test_ts, predictions))print('Test RMSE: %.3f' % rmse)# 绘制图像plt.figure(figsize=(10, 6))plt.plot(train_ts, label='Train')plt.plot(test_ts, label='Test')plt.plot(test_ts.index, predictions, label='Predictions')plt.xlabel('Time/h')plt.ylabel('kwh')plt.title('ARIMA Model Predictions')plt.legend()plt.show()以上代码运行报错如下ValueError: 0 is not in range，请修正代码

rmse = np.sqrt(mean_squared_error(test_ts, predictions)) print('Test RMSE: %.3f' % rmse) # 绘制图像 plt.figure(figsize=(10, 6)) plt.plot(train_ts, label='Train') plt.plot(test_ts, label='Test') plt....

import pandas as pd import matplotlib.pyplot as plt from statsmodels.tsa.api import ExponentialSmoothing from sklearn.metrics import mean_squared_error, mean_absolute_error import numpy as np # 设置中文字体 plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False # 读取数据并处理时间格式 df = pd.read_excel(r'C:\Users\kiki\Desktop\data\2000-2021年R&D.xls') df['Year'] = pd.to_datetime(df['Year'], format='%Y') df.set_index('Year', inplace=True) series = df['R&D%'] # 绘制时间序列图 plt.figure(figsize=(10, 4)) series.plot(title='R&D% 时间序列 (2000-2021年)') plt.grid(True) plt.xticks(rotation=45) plt.tight_layout() plt.show() # 模型比较 models = [ ('Holt Additive', ExponentialSmoothing( series, trend='add', seasonal=None, initialization_method="estimated" )), ('Holt Multiplicative', ExponentialSmoothing( series, trend='mul', seasonal=None, initialization_method="estimated" )), ('Holt Additive Damped', ExponentialSmoothing( series, trend='add', damped_trend=True, initialization_method="estimated" )), ] best_aic = float('inf') best_model = None best_name = '' print("模型对比结果：") for name, model in models: try: results = model.fit() aic = results.aic fitted_values = results.fittedvalues # 计算指标 mse = mean_squared_error(series, fitted_values) rmse = np.sqrt(mse) mae = mean_absolute_error(series, fitted_values) # 计算MAPE（处理零值） if (series == 0).any(): mape = np.nan mape_msg = "MAPE: NaN (含零值)" else: mape = np.mean(np.abs((series - fitted_values) / series)) * 100 mape_msg = f"MAPE: {mape:.2f}%" print( f"{name.ljust(20)} | AIC: {aic:.2f} | " f"MSE: {mse:.6f} | RMSE: {rmse:.6f} | " f"MAE: {mae:.6f} | {mape_msg}" ) if aic < best_aic: best_aic = aic best_model = results best_name = nam

import matplotlib.pyplot as plt plt.figure(figsize=(12,6)) plt.plot(train.index, train, label='Train') plt.plot(test.index, test, label='Actual') plt.plot(test.index, forecast, label='Forecast') plt....

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from statsmodels.tsa.arima.model import ARIMA residuals = original_data - predictions arima_model = ARIMA(residuals, order=(1,1,1)).fit() corrected_predictions = predictions + arima_model.forecast...

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修改润色代码，让代码更完整，更符合科研论文标准：import os import pandas as pd import numpy as np import matplotlib.pyplot as plt import statsmodels.api as sm from statsmodels.tsa.stattools import adfuller from statsmodels.graphics.tsaplots import plot_acf, plot_pacf from statsmodels.tsa.arima.model import ARIMA from scipy.stats import shapiro from statsmodels.stats.diagnostic import acorr_ljungbox import seaborn as sns import warnings import itertools from sklearn.metrics import mean_absolute_error, mean_squared_error from dateutil.relativedelta import relativedelta # 忽略特定类型的警告 warnings.filterwarnings('ignore', category=FutureWarning) # 设置中文字体和负号显示 plt.rcParams['font.family'] = 'SimHei' plt.rcParams['axes.unicode_minus'] = False # ====================== # 数据处理 # ====================== data_folder = 'E:\\桌面\\大论文\\数据' data_path = os.path.join(data_folder, '预测数据3.0.xlsx') # 构造完整的文件路径 try: # 读取 Excel 文件 df = pd.read_excel(data_path) except FileNotFoundError: print(f"文件 {data_path} 未找到，请检查文件路径。") exit(1) # 提取需要的列 ts = df[['年份', '发病率']] print("原始数据预览:") print(ts.head()) # 数据划分 n_sample = ts.shape[0] n_train = int(0.7 * n_sample) # 70% 训练数据 n_test = n_sample - n_train # 30% 测试数据 ts_train = ts.iloc[:n_train].set_index('年份')['发病率'] # 训练集 ts_test = ts.iloc[n_train:].set_index('年份')['发病率'] # 测试集 # 绘制原始时间序列图 plt.figure(figsize=(12, 6)) plt.plot(ts['年份'], ts['发病率'], 'o-', color='blue', linewidth=2, markersize=6) plt.title('2005-2023年乙型肝炎发病率趋势', fontsize=14) plt.xlabel('年份', fontsize=12) plt.ylabel('发病率（/10万）', fontsize=12) plt.grid(True, linestyle='--', alpha=0.7) plt.tight_layout() plt.show() # ====================== # 数据平稳化处理 # ====================== # 一阶差分 ts_diff = ts['发病率'].diff().dropna() ts_train_diff = ts_diff.iloc[:n_train - 1] # 训练集差分 # 绘制差分后序列 plt.figure(figsize=(12, 6)) plt.plot(ts['年份'][1:], ts_diff, 'o-', color='green', linewidth=2, markersize=6) plt.title('发病率一阶差分序列', fontsize=14) plt.xlabel('年份', fontsize=12) plt.ylabel('发病率差分值', fontsize=12) plt.grid(True, linestyle='--', alpha=0.7) plt.tight_layout() plt.show() # ====================== # 平稳性检验 # ====================== def adf_test(series, title=''): """执行ADF检验并输出结果""" result = adfuller(series.dropna()) print(f'\n{title}ADF检验结果:') print(f'ADF统计量: {result[0]:.4f}') print(f'p值: {result[1]:.4f}') print('临界值:') for key, value in result[4].items(): print(f'\t{key}: {value:.4f}') print('是否平稳: ' + ('是' if result[1] <= 0.05 else '否')) adf_test(ts['发病率'], '原始数据') adf_test(ts_diff, '一阶差分后数据') # ====================== # 白噪声检验 # ====================== def white_noise_test(series, lags=6): """执行白噪声检验""" lb_test = acorr_ljungbox(series.dropna(), lags=lags) p_values = lb_test['lb_pvalue'] is_white_noise = all(p_values > 0.05) print(f'\n白噪声检验结果 (滞后阶数 {lags}):') print('p值:', p_values.values) print('是否白噪声:', is_white_noise) white_noise_test(ts['发病率']) white_noise_test(ts_diff) # ====================== # 自相关和偏自相关分析 # ====================== fig, ax = plt.subplots(2, 1, figsize=(12, 10)) plot_acf(ts_diff, lags=20, ax=ax[0]) ax[0].set_title('自相关函数(ACF)', fontsize=14) plot_pacf(ts_diff, lags=20, ax=ax[1], method='ywm') ax[1].set_title('偏自相关函数(PACF)', fontsize=14) plt.tight_layout() plt.show() # ====================== # ARIMA参数优化 (固定d=0) # ====================== print("\n开始ARIMA参数网格搜索...") pq_range = range(0, 5) # 减少搜索范围提高效率 results_bic = pd.DataFrame(columns=pq_range, index=pq_range) for p, q in itertools.product(pq_range, pq_range): if p == 0 and q == 0: results_bic.loc[p, q] = np.nan continue try: model = ARIMA(ts_train_diff, order=(p, 0, q)) res = model.fit() results_bic.loc[p, q] = res.bic except Exception as e: results_bic.loc[p, q] = np.nan # 找到最优参数 results_bic = results_bic.astype(float) min_bic = results_bic.min().min() best_p, best_q = results_bic.stack().idxmin() print(f'\n最优参数: p={best_p}, q={best_q}, BIC={min_bic:.2f}') # 绘制BIC热力图 plt.figure(figsize=(10, 8)) sns.heatmap(results_bic, annot=True, fmt=".1f", cmap="coolwarm", cbar_kws={'label': 'BIC值'}, mask=results_bic.isnull()) plt.title('ARIMA模型BIC值热力图', fontsize=14) plt.xlabel('q值', fontsize=12) plt.ylabel('p值', fontsize=12) plt.tight_layout() plt.show() # ====================== # 模型训练 # ====================== print(f"\n训练ARIMA({best_p},1,{best_q})模型...") model = ARIMA(ts_train, order=(best_p, 1, best_q)) # 使用原始数据，d=1 result = model.fit() print(result.summary()) # ====================== # 模型诊断 # ====================== residuals = result.resid # 残差正态性检验 plt.figure(figsize=(12, 10)) plt.subplot(2, 2, 1) residuals.plot(ax=plt.gca()) plt.title('残差序列', fontsize=12) plt.grid(True, alpha=0.3) plt.subplot(2, 2, 2) sm.qqplot(residuals, line='45', fit=True, ax=plt.gca()) plt.title('残差QQ图', fontsize=12) plt.subplot(2, 2, 3) sns.histplot(residuals, kde=True, ax=plt.gca()) plt.title('残差分布', fontsize=12) plt.subplot(2, 2, 4) plot_acf(residuals, lags=20, ax=plt.gca()) plt.title('残差ACF', fontsize=12) plt.suptitle('模型残差诊断', fontsize=16) plt.tight_layout() plt.show() # 残差白噪声检验 white_noise_test(residuals) # ====================== # 模型预测 # ====================== # 测试集预测 forecast = result.get_forecast(steps=n_test) forecast_mean = forecast.predicted_mean forecast_ci = forecast.conf_int() # 未来预测 (2024年) future_years = 1 future_forecast = result.get_forecast(steps=n_test + future_years) future_mean = future_forecast.predicted_mean[-future_years:] future_ci = future_forecast.conf_int()[-future_years:] # 生成未来年份 last_year = ts['年份'].iloc[-1] future_years_list = [last_year + i for i in range(1, future_years + 1)] # 合并结果 all_years = np.concatenate([ts['年份'], future_years_list]) all_actual = np.concatenate([ts['发病率'], [np.nan] * future_years]) all_forecast = np.concatenate([ np.full(n_train, np.nan), forecast_mean.iloc[:n_test], future_mean ]) # ====================== # 模型评估 # ====================== test_actual = ts_test.values test_pred = forecast_mean.iloc[:n_test].values mae = mean_absolute_error(test_actual, test_pred) rmse = np.sqrt(mean_squared_error(test_actual, test_pred)) mape = np.mean(np.abs((test_actual - test_pred) / test_actual)) * 100 print("\n模型测试集性能:") print(f"MAE: {mae:.4f}") print(f"RMSE: {rmse:.4f}") print(f"MAPE: {mape:.2f}%") # ====================== # 可视化结果 # ====================== plt.figure(figsize=(16, 8)) # 历史数据 plt.plot(ts['年份'], ts['发病率'], 'o-', label='历史实际值', color='#1f77b4', linewidth=2, markersize=8) # 测试集预测 plt.plot(ts_test.index, test_pred, 's--', label='测试集预测值', color='#ff7f0e', linewidth=2, markersize=8) # 未来预测 plt.plot(future_years_list, future_mean, 'D-', label='2024年预测值', color='#d62728', linewidth=2, markersize=8) # 置信区间 plt.fill_between( np.concatenate([ts_test.index, future_years_list]), np.concatenate([forecast_ci.iloc[:, 0], future_ci.iloc[:, 0]]), np.concatenate([forecast_ci.iloc[:, 1], future_ci.iloc[:, 1]]), color='gray', alpha=0.2, label='95%置信区间' ) # 分界线 plt.axvline(x=ts_test.index[0], color='gray', linestyle='--', label='预测起点') plt.axvline(x=last_year, color='purple', linestyle=':', label='当前年份') plt.title('乙型肝炎发病率ARIMA模型预测', fontsize=16) plt.xlabel('年份', fontsize=12) plt.ylabel('发病率(/10万)', fontsize=12) plt.legend(loc='best') plt.grid(True, linestyle='--', alpha=0.7) plt.tight_layout() plt.show() # ====================== # 输出预测结果 # ====================== print("\n2024年预测结果:") future_df = pd.DataFrame({ '年份': future_years_list, '预测发病率': future_mean.values, '下限': future_ci.iloc[:, 0].values, '上限': future_ci.iloc[:, 1].values }) print(future_df) # ====================== # 拟合值分析 # ====================== fitted_values = result.fittedvalues plt.figure(figsize=(14, 7)) plt.plot(ts['年份'], ts['发病率'], 'o-', label='实际值', color='#1f77b4') plt.plot(fitted_values.index, fitted_values, 's--', label='拟合值', color='#ff7f0e') residuals = ts['发病率'] - fitted_values.reindex(ts['年份']).values for year, actual, fitted in zip(ts['年份'], ts['发病率'], fitted_values): plt.plot([year, year], [actual, fitted], 'r--', alpha=0.3) plt.title('模型拟合效果 (2005-2023年)', fontsize=16) plt.xlabel('年份', fontsize=12) plt.ylabel('发病率(/10万)', fontsize=12) plt.legend() plt.grid(True, alpha=0.3) plt.tight_layout() plt.show() # 拟合残差分析 plt.figure(figsize=(14, 6)) plt.bar(ts['年份'], residuals, color='#2ca02c', alpha=0.7) plt.axhline(0, color='r', linestyle='--') plt.title('模型拟合残差', fontsize=16) plt.xlabel('年份', fontsize=12) plt.ylabel('残差值', fontsize=12) plt.grid(True, alpha=0.3) plt.tight_layout() plt.show()

raise ResearchException("E102: 数据范围越界") from e #### 工具推荐 1. 静态检查: flake8 + mypy 2. 文档生成: Sphinx + numpydoc 3. 性能分析: cProfile + line_profiler > 科研代码应像论文...

Traceback (most recent call last): File "F:\pyfile\MCM\school\Q4\Q4.py", line 193, in <module> gas_metrics, gas_series, gas_preds = evaluate_improvement(opt_model, df, '汽油') File "F:\pyfile\MCM\school\Q4\Q4.py", line 156, in evaluate_improvement raw_series, opt_preds, lstm_model, X_val, y_val = optimized_model.ensemble_predict(df, fuel_type) File "F:\pyfile\MCM\school\Q4\Q4.py", line 141, in ensemble_predict lstm_preds = self.scalers[fuel_type].inverse_transform( File "F:\Anaconda3\envs\pytorch_gpu_12_1\lib\site-packages\sklearn\preprocessing\_data.py", line 581, in inverse_transform X -= self.min_ ValueError: non-broadcastable output operand with shape (1095,1) doesn't match the broadcast shape (1095,7)

from statsmodels.tsa.arima.model import ARIMA from statsmodels.tsa.seasonal import STL from datetime import datetime, timedelta import os import warnings # 中文显示设置 plt.rcParams['font.sans-serif'...

Traceback (most recent call last): File "C:\Users\ASUS\AppData\Roaming\JetBrains\PyCharmCE2023.3\scratches\scratch_17.py", line 72, in <module> adf_test(ts['发病率'], '原始数据') NameError: name 'adf_test' is not defined. Did you mean: 'ts_test'?

from sklearn.metrics import mean_absolute_error, mean_squared_error from dateutil.relativedelta import relativedelta # 忽略特定类型的警告 warnings.filterwarnings('ignore', category=FutureWarning) # ...

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from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score import math import matplotlib.pyplot as plt # 假设你的时间序列数据存储在名为dc的变量中 # 确定差分次数 d = 1 # 一阶差分 ...

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