CPi-PS (Common-Path interferometry with Phase-Shifting)

All selected domains feature publicly accessible datasets: Seismic: USGS Earthquake Catalog (https://earthquake.usgs.gov/), IRIS Seismic Data Portal Weather: NOAA Global Historical Climatology Network (https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily) Financial: Alpha Vantage API, Yahoo Finance (yfinance library), FNSPID dataset Cryptocurrency: CryptoDataDownload, exchange APIs (Binance, Coinbase) This research tests whether physics-informed principles derived from Sagnac common-path interferometry can be effectively transposed to time series noise filtering. The hypothesis posits that analyzing phase coherence in the frequency domain will outperform traditional amplitude-based denoising methods (Extended Kalman Filter, Wavelet Transform, Exponential Smoothing). The core premise is that genuine signal components exhibit consistent phase relationships across frequency bands, while noise components display random phase distributions—enabling more precise discrimination between signal and noise. Data Description Dataset Structure: This dataset contains raw time series data and filtered outputs from a comprehensive validation study of the CPi-PS (Common-Path Interferometry with Phase-Shifting) framework. The study compares four denoising methods across 16 real-world time series spanning five application domains, yielding 64 experimental conditions. Data Sources: Commodity data: Daily prices for Copper, Crude Oil, Gasoline, Gold, Natural Gas, and Silver (6 variables, 18,449 daily observations) Weather data: Daily temperature and precipitation records (2 variables, 2,189 daily observations) Seismic data: Earthquake magnitude measurements (1 variable, 9,415 event-based observations) Cryptocurrency data: Bitcoin and Ethereum daily closing prices (2 variables, 4,435 daily observations) Financial index data: Dow Jones, S&P 500, JPM, MSFT, and TSLA closing prices (5 variables, 7,619 trading day observations) Data Interpretation Guidelines The results validate that transposing interferometric phase coherence principles to frequency domain analysis effectively discriminates signal from noise. The method is particularly effective for time series with underlying deterministic structure (financial data) and less effective for intrinsically stochastic phenomena (seismic events, precipitation). Users should select methods based on domain: CPi-PS is recommended for most applications; WT serves as a strong alternative; EKF suits known linear state-space models; ExpSmooth is not recommended for preprocessing due to phase distortion. Data Usage: Raw data can benchmark new denoising algorithms; filtered outputs serve as preprocessed ML inputs; metrics provide baseline comparisons for method development.所选领域均采用公开可获取的数据集： 地震领域：美国地质调查局（USGS）地震目录（https://earthquake.usgs.gov/）、IRIS地震数据门户 气象领域：美国国家海洋和大气管理局（NOAA）全球历史气候学网络（https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily） 金融领域：Alpha Vantage应用程序接口（API）、雅虎财经（Yahoo Finance，yfinance库）、FNSPID数据集 加密货币领域：CryptoDataDownload、交易所API（币安（Binance）、Coinbase） 本研究旨在验证源自萨格纳克共路干涉术的物理信息原理能否有效迁移至时间序列噪声滤波领域。研究假设：在频域内分析相位相干性，其效果将优于传统基于幅值的去噪方法（扩展卡尔曼滤波（Extended Kalman Filter）、小波变换（Wavelet Transform）、指数平滑（Exponential Smoothing））。核心前提为：真实信号分量在各频带间呈现一致的相位关系，而噪声分量则表现为随机相位分布，借此可实现信号与噪声间更精准的区分。 数据集说明 数据集结构：本数据集包含原始时间序列数据与滤波输出结果，源自对CPi-PS（移相共路干涉术，Common-Path Interferometry with Phase-Shifting）框架的全面验证研究。该研究针对覆盖5个应用领域的16组真实世界时间序列，对比了4种去噪方法，共生成64组实验工况。 数据来源： 大宗商品数据：铜、原油、汽油、黄金、天然气及白银的每日价格（6个变量，共18449条每日观测数据） 气象数据：每日气温与降水记录（2个变量，共2189条每日观测数据） 地震数据：地震震级测量值（1个变量，共9415条基于事件的观测数据） 加密货币数据：比特币与以太坊的每日收盘价（2个变量，共4435条每日观测数据） 金融指数数据：道琼斯工业平均指数、标普500指数、摩根大通（JPM）、微软（MSFT）及特斯拉（TSLA）的收盘价（5个变量，共7619条交易日观测数据） 数据解读指南 研究结果验证了：将干涉相位相干性原理迁移至频域分析，可有效实现信号与噪声的区分。该方法对具备潜在确定性结构的时间序列（如金融数据）尤为有效，而对内在随机现象（如地震事件、降水数据）效果较弱。使用者应根据应用领域选择合适方法：CPi-PS适用于多数场景；小波变换（WT）是优秀替代方案；扩展卡尔曼滤波（EKF）适用于已知线性状态空间模型；指数平滑（ExpSmooth）易引发相位畸变，不建议用于预处理流程。 数据用途：原始数据可用于基准测试新型去噪算法；滤波输出结果可作为预处理后的机器学习（Machine Learning，ML）输入；各项指标可为方法开发提供基准对比依据。