TY - GEN
T1 - IF
T2 - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021
AU - Chatterjee, Sarthak
AU - Das, Subhro
AU - Pequito, Sérgio
PY - 2021
Y1 - 2021
N2 - Most optimization problems lack closed-form solutions of the argument that minimizes a given function, and even if these were available it might be prohibitive to compute it. As such, we rely on iterative numerical algorithms to find an approximate solution. In this paper, we propose to leverage fractional calculus in the context of time series analysis methods to devise a new iterative algorithm. Specifically, we propose to leverage autoregressive fractional-order integrative moving average time series, whose coefficients encode a proxy for local spatial information. We provide evidence that our algorithm is efficient and particularly suitable for cases where the Hessian is ill-conditioned.
AB - Most optimization problems lack closed-form solutions of the argument that minimizes a given function, and even if these were available it might be prohibitive to compute it. As such, we rely on iterative numerical algorithms to find an approximate solution. In this paper, we propose to leverage fractional calculus in the context of time series analysis methods to devise a new iterative algorithm. Specifically, we propose to leverage autoregressive fractional-order integrative moving average time series, whose coefficients encode a proxy for local spatial information. We provide evidence that our algorithm is efficient and particularly suitable for cases where the Hessian is ill-conditioned.
UR - http://www.scopus.com/inward/record.url?scp=85129305654&partnerID=8YFLogxK
U2 - 10.14428/esann/2021.ES2021-133
DO - 10.14428/esann/2021.ES2021-133
M3 - Conference contribution
AN - SCOPUS:85129305654
T3 - ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
SP - 641
EP - 646
BT - ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
PB - i6doc.com publication
Y2 - 6 October 2021 through 8 October 2021
ER -