import numpy as npimport pandas as pdimport yfinance as yfimport pywtimport matplotlib.pyplot as pltimport ossp500 = yf.download('^GSPC', start='2020-01-01', end='2024-08-31')returns = np.log(sp500['Close']).diff.dropnawavelet = 'db8' level = 5coeffs = pywt.wavedec(returns, wavelet, mode='per', level=level)plt.style.use('dark_background')def add_textbox(ax, text, fontsize, fontweight='normal'):ax.text(0.02, 0.95, text, transform=ax.transAxes, fontsize=fontsize, fontweight=fontweight,verticalalignment='top', bbox=dict(boxstyle='round', facecolor='black', edgecolor='white', alpha=0.7))fig1, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 10), sharex=True)fig1.suptitle('S&P 500 Log Returns and Long-term Trend', fontsize=20, fontweight='bold', y=0.98)ax1.plot(returns.index, returns, color='cyan', lw=1.5)add_textbox(ax1, 'Original Signal', 14, 'bold')ax1.set_ylabel('Log Returns', fontsize=12)# A5reconstructed_approx = pywt.waverec([coeffs[0]] + [np.zeros_like(c) for c in coeffs[1:]], wavelet, mode='per')ax2.plot(returns.index, reconstructed_approx[:len(returns)], color='red', lw=1.5)add_textbox(ax2, 'Approximation (A5)', 14, 'bold')ax2.set_ylabel('A5', fontsize=12)ax2.set_xlabel('Date', fontsize=12)for ax in (ax1, ax2):ax.tick_params(axis='x', rotation=45)ax.grid(True, color='gray', linestyle='--', linewidth=0.5)plt.tight_layout(rect=[0, 0.02, 1, 0.96])returns_trend_path = os.path.join(current_dir, 'sp500_returns_and_trend.png')plt.savefig(returns_trend_path, dpi=300, bbox_inches='tight')plt.showplt.close摘要：import numpy as npimport pandas as pdimport yfinance as yfimport pywtimport matplotlib.pyplot as pltimport ossp500 = yf.download('
Python环境下基于最大离散重叠小波变换和支持向量回归的金融时间序列预测
import numpy as npimport pandas as pdimport copyimport matplotlib.pyplot as pltfrom sklearn.model_selection import train_test_splitfrom sklearn import svmfrom sklearn.metrics import mean_squared_errorfrom numpy.lib.stride_tricks import sliding_window_viewfrom modwt import modwt, modwtmra,imodwt完整代码：
使用时间序列分析方法预测比特币价格（密集神经网络、卷积网络、LSTM网络等方法，Python，ipynb文件)
压缩包文件包括Jupyter Notebook文件（Python编写）和数据。
完整代码：
基于深度学习的股票市场价格（时间序列）预测（Python，ipynb文件）
基于注意力机制长短期记忆（LSTM）网络的通用电气股票价格预测（Python,Keras）
Python环境下基于VMD-Attention-LSTM模型收盘价预测深度学习模型
import numpy as npimport pandas as pdfrom sklearn.preprocessing import MinMaxScalerimport tensorflow as tfimport models.vmd_attention_lstm as mvimport utilt.VMD as vmdzi来源：彬琪教育
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