165 lines
5.3 KiB
Python
165 lines
5.3 KiB
Python
import os
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import matplotlib as mpl
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mpl.rcParams['font.family'] = 'SimHei' # 设置字体为黑体
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import random
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import string
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import time
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import base64
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import requests
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from hashlib import sha256
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from hmac import HMAC
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from math import sqrt
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.metrics import mean_squared_error
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from keras.models import Sequential
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from keras.layers import GRU, Dense, Dropout
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from keras.optimizers import Adam
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from keras.callbacks import EarlyStopping
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# 数据获取和预处理部分
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from sklearn.preprocessing import MinMaxScaler
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import pandas as pd
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import datetime
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import string
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import base64
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import requests
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import random
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import time
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import re
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import hmac
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import hashlib
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def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
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'''
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将时间序列数据转换为监督学习数据
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:param data:数据集
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:param n_in: 输入序列长度，默认为1
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:param n_out:输出序列长度，默认为1
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:param dropnan:
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:return:
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'''
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n_vars = 1 if type(data) is list else data.shape[1]
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df = pd.DataFrame(data)
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cols, names = list(), list()
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# input sequence (t-n, ... t-1)
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# 将3组输入数据依次向下移动3，2，1行，将数据加入cols列表（技巧：(n_in, 0, -1)中的-1指倒序循环，步长为1）
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for i in range(n_in, 0, -1):
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cols.append(df.shift(i))
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names += [('var%d(t-%d)' % (j + 1, i)) for j in range(n_vars)]
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# forecast sequence (t, t+1, ... t+n)
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# 将一组输出数据加入cols列表（技巧：其中i=0）
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for i in range(0, n_out):
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cols.append(df.shift(-i))
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if i == 0:
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names += [('var%d(t)' % (j + 1)) for j in range(n_vars)]
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else:
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names += [('var%d(t+%d)' % (j + 1, i)) for j in range(n_vars)]
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# cols列表(list)中现在有四块经过下移后的数据(即：df(-3),df(-2),df(-1),df)，将四块数据按列 并排合并
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agg = pd.concat(cols, axis=1)
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# 给合并后的数据添加列名
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agg.columns = names
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# 删除NaN值列
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if dropnan:
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agg.dropna(inplace=True)
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return agg
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def ex_GRU(df):
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dataset = df.copy()
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dataset.set_index('ds', inplace=True)
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values = dataset.values
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# 标准化/放缩 特征值在（0,1）之间
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scaler = MinMaxScaler(feature_range=(0, 1))
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scaled = scaler.fit_transform(values)
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# 数据准备
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n_days = 14 # 使用过去14天的数据
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n_features = scaled.shape[1] # 特征数量根据实际数据调整
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reframed = series_to_supervised(scaled, n_days, 1)
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# 划分训练集和测试集
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values = reframed.values
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n_train_days = int(values.shape[0] * 0.8)
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train = values[:n_train_days, :]
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test = values[n_train_days:, :]
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# 输入输出数据
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n_obs = n_days * n_features
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train_X, train_y = train[:, :n_obs], train[:, -n_features]
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test_X, test_y = test[:, :n_obs], test[:, -n_features]
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# 输入数据重塑为 [样本数, 时间步长, 特征数]
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train_X = train_X.reshape((train_X.shape[0], n_days, n_features))
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test_X = test_X.reshape((test_X.shape[0], n_days, n_features))
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# 构造GRU模型
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model = Sequential()
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model.add(GRU(50, return_sequences=True, input_shape=(n_days, n_features)))
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model.add(Dropout(0.2))
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model.add(GRU(50))
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model.add(Dropout(0.2))
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model.add(Dense(1))
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# 编译模型
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optimizer = Adam(learning_rate=0.001)
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model.compile(loss='mean_squared_error', optimizer=optimizer)
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# 定义回调函数
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early_stopping = EarlyStopping(monitor='val_loss', patience=10)
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# 训练模型
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history = model.fit(train_X, train_y, epochs=100, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False, callbacks=[early_stopping])
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# 执行预测
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yhat = model.predict(test_X)
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test_X = test_X.reshape((test_X.shape[0], n_days * n_features))
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# 将预测列和真实列数据逆归一化
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inv_yhat = np.concatenate((yhat, test_X[:, -n_features+1:]), axis=1)
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inv_yhat = scaler.inverse_transform(inv_yhat)
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inv_yhat = inv_yhat[:, 0]
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test_y = test_y.reshape((len(test_y), 1))
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inv_y = np.concatenate((test_y, test_X[:, -n_features+1:]), axis=1)
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inv_y = scaler.inverse_transform(inv_y)
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inv_y = inv_y[:, 0]
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# 计算RMSE
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rmse = sqrt(mean_squared_error(inv_y, inv_yhat))
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print('Test RMSE: %.3f' % rmse)
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# 可视化结果
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n = 150
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time_axis_data = np.array(range(n))
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time_axis_future = np.array(range(n + 7))
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inv_y = inv_y[-n:]
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inv_yhat = inv_yhat[-n-7:]
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fig, ax = plt.subplots(2, 1, gridspec_kw={'height_ratios': [5, 4]})
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fig.set_size_inches(8, 6)
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ax[0].plot(time_axis_data, inv_y, label='历史价格')
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ax[0].plot(time_axis_future, inv_yhat, linestyle='dashed', label='预测价格')
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ax[0].set_xlabel('时间')
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ax[0].set_ylabel('价格')
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ax[0].legend()
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ax[0].set_title('布伦特_多价格预测')
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ax[0].set_ylim(min(inv_y[-n - 7:]) * 0.4, max(inv_y[-n - 7:]) * 1.6)
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ax[1].axis('off')
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table_data = [[f"Day {i + 1}", "{:.2f}".format(val)] for i, val in enumerate(inv_yhat[-7:])]
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table = ax[1].table(cellText=table_data, colLabels=['Day', 'Prediction'], loc='center')
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table.auto_set_font_size(True)
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filename = os.path.basename(__file__).split('.')[0]
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plt.savefig(filename + '.png')
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plt.show()
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