Using Orthogonal Matching Pursuit Algorithm in Time Series

Using Python to Analyze and Predict Time Series With OMP

Sofien Kaabar, CFA


Photo by Pietro Jeng on Unsplash

Orthogonal Matching Pursuit (OMP) is a technique used in signal processing and data analysis to identify the most important components of a signal or dataset. It works by iteratively selecting components that are most correlated with the remaining signal, while ensuring that the selected components are orthogonal (meaning they are as different from each other as possible).

Testing OMP on stationary time series could be interesting because it allows us to see how well OMP can extract meaningful information from a type of data commonly encountered in fields like finance and economics. Stationary time series have statistical properties that remain constant over time, making them suitable for analyzing trends, patterns, and relationships.

By applying OMP to stationary time series, we can assess its effectiveness in identifying important features, such as underlying trends or periodic behavior, which could be valuable for tasks like forecasting or anomaly detection.

This article presents a simple way to create the OMP model in Python to predict the returns of the S&P 500 index.

Introduction to Orthogonal Matching Pursuit