下面是一个简化示例,展示如何利用Scikit-learn的状态空间模型框架来定义一个简单的自回归模型,这与ARIMA模型中的AR部分类似。 importnumpyasnpfromscipy.signalimportlfilterfromsklearn.linear_modelimportLinearRegressionfromsklearn.metricsimportmean_squared_errorfromsklearn.preprocessingimportPolynomialFeatures# 生成模拟数据...
from statsmodels.tsa.stattools import adfuller from sklearn.metrics import mean_squared_error import numpy as np import pandas as pd import warnings warnings.filterwarnings('ignore') from core.llms.llm_factory import LLMFactory from dealer.stock_data_provider import StockDataProvider llm_client = LLM...
滚动预测 ARIMA 模型 我们的数据集已拆分为训练集和测试集，我们继续训练 ARIMA 模型。然后预测了第一个预测。通用ARIMA模型的结果很差，因为它产生了一条平线。因此，我们决定尝试滚动预测方法。注意：代码示例是 BOGDAN IVANYUK 笔记本的修改版本。from statsmodels.tsa.arima.model import ARIMAfrom sklearn.metrics ...
fromstatsmodels.tsa.arima.modelimportARIMAfromsklearn.metricsimportmean_squared_error,mean_absolute_errorimportmathtrain_data,test_data=net_df[0:int(len(net_df)*0.9)],net_df[int(len(net_df)*0.9):]train_arima=train_data['Open']test_arima=test_data['Open']history=[xforxintrain_arima]y=te...
from sklearn.model_selection import KFold kf = KFold(n_splits=5) for train_index, val_index in kf.split(X_train): X_train_fold, X_val_fold = X_train[train_index], X_train[val_index] y_train_fold, y_val_fold = y_train[train_index], y_train[val_index] ...
from sklearn.metrics import mean_squared_error, mean_absolute_error 预测训练集 predictions = model_fit.predict(start=0, end=len(data)-1) mse = mean_squared_error(data['value_column'], predictions) mae = mean_absolute_error(data['value_column'], predictions) ...
我使用 sklearn 的 MinMaxScaler 将数据从 0 缩放到 1：我使用 statsmodels 的 ARIMA 模型来训练数据：我对测试集进行了预测，将其绘制在图表上，并获得了均方根误差 (RMSE)：ARIMA:Mean Absolute Error: 4137.746710432508 Root Mean Square Error: 5018.369921709136 Tensorflow 中的 GRU 和 LSTM 代码 我读取...
from sklearn.metrics import mean_squared_error from mango import scheduler, Tuner def arima_objective_function(args_list): global data_values params_evaluated = [] results = [] for params in args_list: try: p,d,q = params['p'],params['d'], params['q'] ...
这里我们将使用pandas库来处理时间序列数据,statsmodels库来构建和拟合ARIMA模型。 1. 导入必要的Python库 首先,你需要导入pandas和statsmodels库中的相关模块: python import pandas as pd import numpy as np from statsmodels.tsa.arima.model import ARIMA from sklearn.metrics import mean_squared_error import ...
from sklearn.metricsimportmean_squared_error, mean_absolute_errorimportmath train_data, test_data = net_df[0:int(len(net_df)*0.9)], net_df[int(len(net_df)*0.9):] train_arima = train_data['Open'] test_arima = test_data['Open'] ...