arXiv subjects

Ivan Ricardo

Publications and source records attributed to Ivan Ricardo.

4 recordsLinked to original sources

arXiv2026

Performance of radio-based detection to operational monitoring M5+ class solar flares

Early detection of major solar flares is critical for defense operations due to their potential to disturb radar and radio systems. Typically, soft X-ray flux is used to monitor and classify solar flares, but since this flux has to be measured in space, it means that its availability itself is dependent on space weather conditions. For this reason, in this paper, we investigated the feasibility of using ground radio observations to monitor major (M5+ class) solar flares. We made use of datasets from the GOES-16 satellite and the Radio Solar Telescope Network in the time range between March 2023 and March 2025. An elastic net regularized logistic regression model was trained on this data, optimized through a grid search and with incorporated class weighting for class imbalance. It was found that especially higher frequencies (8800 MHz) had a reasonable ability in monitoring and predicting major flares (precision and recall for flare events are 53% and 65%, respectively - implying that roughly one third of flares were not detected - with signals appearing, on average, 3 to 4 minutes before the M5 threshold is exceeded). Radio measurements at super high frequencies can thus serve as an alternative method to monitor major solar flaring activity.

astro-ph.SR↗

arXiv2025

Decomposing Co-Movements in Matrix-Valued Time Series: A Pseudo-Structural Reduced-Rank Approach

A pseudo-structural framework is proposed for analyzing contemporaneous co-movements in stationary reduced-rank matrix autoregressive (RRMAR) models. Unlike conventional vector autoregressive (VAR) models that discard the matrix structure, the formulation preserves it, enabling a decomposition of co-movements into three interpretable components: row-specific, column-specific, and joint (row--column) interactions across the matrix-valued time series. The estimator admits standard asymptotic inference and a BIC-type criterion is proposed for the joint selection of the reduced ranks and the autoregressive lag order. The method's finite-sample performance in terms of estimation accuracy, coverage, and rank selection is validated through simulation experiments, including cases of rank misspecification. Practical usefulness is illustrated through an application to labor market data from nine Midwestern U.S. states, revealing distinct row-, column-, and joint co-movement patterns.

econ.EM↗

arXiv2024

Detecting Cointegrating Relations in Non-stationary Matrix-Valued Time Series

This paper proposes a Matrix Error Correction Model to identify cointegration relations in matrix-valued time series. We hereby allow separate cointegrating relations along the rows and columns of the matrix-valued time series and use information criteria to select the cointegration ranks. Through Monte Carlo simulations and a macroeconomic application, we demonstrate that our approach provides a reliable estimation of the number of cointegrating relationships.

econ.EM↗

arXiv2024

Reduced-Rank Matrix Autoregressive Models: A Medium $N$ Approach

Reduced-rank regressions are powerful tools used to identify co-movements within economic time series. However, this task becomes challenging when we observe matrix-valued time series, where each dimension may have a different co-movement structure. We propose reduced-rank regressions with a tensor structure for the coefficient matrix to provide new insights into co-movements within and between the dimensions of matrix-valued time series. Moreover, we relate the co-movement structures to two commonly used reduced-rank models, namely the serial correlation common feature and the index model. Two empirical applications involving U.S.\ states and economic indicators for the Eurozone and North American countries illustrate how our new tools identify co-movements.

econ.EM↗