智慧交通数据分析系统 python 时间序列预测算法 爬虫 出行速度预测 拥堵预测

import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from statsmodels.tsa.arima_model import ARIMA
from fbprophet import Prophet
 
# 读取数据
data = pd.read_csv('data.csv')
 
# 数据预处理
scaler = MinMaxScaler()
data_scaled = scaler.fit_transform(data['count'].values.reshape(-1, 1))
data_scaled = pd.DataFrame(data_scaled, columns=['count_scaled'])
 
# 训练ARIMA模型
def train_arima(data, p, d, q):
    model = ARIMA(data, order=(p, d, q))
    model_fit = model.fit()
    return model_fit
 
# 使用Prophet模型
def train_prophet(data):
    model = Prophet()
    data['y'] = data['count_scaled']
    model.fit(data[['ds', 'y']])
    return model
 
# 预测
def predict(model, steps_ahead):
    future = model.make_future_dataframe(periods=steps_ahead)
    forecast = model.predict(future)
    return scaler.inverse_transform(forecast['yhat'].values)
 
# 选择合适的ARIMA参数
p, d, q = 0, 1, 1  # 示例参数
model_arima = train_arima(data_scaled, p, d, q)
forecast_arima = predict(model_arima, 30)  # 预测30天
 
# 使用Prophet模型进行预测
model_prophet = train_prophet(data_scaled)
forecast_prophet = predict(model_prophet, 30)  # 预测30天
 
# 计算MSE
mse_arima = mean_squared_error(data_scaled['count_scaled'].values, forecast_arima)
mse_prophet = mean_squared_error(data_scaled['count_scaled'].values, forecast_prophet)
 
# 输出结果
print(f"ARIMA MSE: {mse_arima}")
print(f"Prophet MSE: {mse_prophet}")

这段代码展示了如何使用ARIMA和Prophet模型进行时间序列预测，并计算预测的平均平方误差（MSE）。这是一个实用的教学示例，可以帮助开发者理解如何在实际应用中应用时间序列分析方法。