6533b7d4fe1ef96bd126304b

RESEARCH PRODUCT

Robust Graph Topology Learning and Application in Stock Market Inference

Mahmoud Ramezani-mayiamiKarl Skretting

subject

Graph signal processingComputer scienceTicker symbolInference020206 networking & telecommunications02 engineering and technology020204 information systemsOutlier0202 electrical engineering electronic engineering information engineeringGraph (abstract data type)Topological graph theoryStock marketTime domainAlgorithm

description

In many applications, there are multiple interacting entities, generating time series of data over the space. To describe the relation within the set of data, the underlying topology may be used. In many real applications, not only the signal/data of interest is measured in noise, but it is also contaminated with outliers. The proposed method, called RGTL, infers the graph topology from noisy measurements and removes these outliers simultaneously. Here, it is assumed that we have no information about the space graph topology, while we know that graph signal are sampled consecutively in time and thus the graph in time domain is given. The simulation results show that the proposed algorithm has a better performance for different graph orders, compared with the conventional graph topology inference methods. Due to the nature of stock market data and the presence of noise and high power outliers, the proposed method is also applied to find some relations among selected ticker symbol prices in the USA market.

yearjournalcountryeditionlanguage
2019-09-012019 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)
https://doi.org/10.1109/icsipa45851.2019.8977789