000015225 001__ 15225
000015225 005__ 20250107133955.0
000015225 022__ $$a2364-415X
000015225 0247_ $$2DOI$$a10.1007/s41060-024-00690-y
000015225 037__ $$aARTICLE
000015225 039_9 $$a2025-01-07 13:39:55$$b0$$c2025-01-06 13:33:19$$d1000305$$c2024-12-20 15:28:35$$d0$$y2024-12-20 15:28:27$$z1000099
000015225 041__ $$aeng
000015225 245__ $$aChain-structured neural architecture search for financial time series forecasting
000015225 260__ $$aBerlin, Germany$$bSpringer Nature
000015225 269__ $$a2024-12
000015225 300__ $$a10 p.
000015225 506__ $$avisible
000015225 520__ $$9eng$$aNeural architecture search (NAS) emerged as a way to automatically optimize neural networks for a specific task and dataset. Despite an abundance of research on NAS for images and natural language applications, similar studies for time series data are lacking. Among NAS search spaces, chain-structured are the simplest and most applicable to small datasets like time series. We compare three popular NAS strategies on chain-structured search spaces: Bayesian optimization (specifically Tree-structured Parzen Estimator), the hyperband method, and reinforcement learning in the context of financial time series forecasting. These strategies were employed to optimize simple well-understood neural architectures like the MLP, 1D CNN, and RNN, with more complex temporal fusion transformers (TFT) and their own optimizers included for comparison. We find Bayesian optimization and the hyperband method performing best among the strategies, and RNN and 1D CNN best among the architectures, but all methods were very close to each other with a high variance due to the difficulty of working with financial datasets. We discuss our approach to overcome the variance and provide implementation recommendations for future users and researchers.
000015225 540__ $$acorrect
000015225 592__ $$aHEIG-VD
000015225 592__ $$bIICT - Institut des Technologies de l'Information et de la Communication
000015225 592__ $$cIngénierie et Architecture
000015225 6531_ $$9eng$$aneural architecture search
000015225 6531_ $$9eng$$atime series forecasting
000015225 6531_ $$9eng$$ahyperparameter optimization
000015225 6531_ $$9eng$$adeep learning
000015225 6531_ $$9eng$$aneural networks
000015225 6531_ $$9eng$$areinforcement learning
000015225 655__ $$ascientifique
000015225 700__ $$aLevchenko, Denis$$uSchool of Engineering and Management Vaud, HES-SO University of Applied Sciences and Arts Western Switzerland
000015225 700__ $$aRappos, Efstratios$$uSchool of Engineering and Management Vaud, HES-SO University of Applied Sciences and Arts Western Switzerland
000015225 700__ $$aAtaee, Shabnam$$uSchool of Engineering and Management Vaud, HES-SO University of Applied Sciences and Arts Western Switzerland
000015225 700__ $$aNigro, Biagio$$uPredictive Layer SA, Rolle, Switzerland
000015225 700__ $$aRobert-Nicoud, Stephan$$uSchool of Engineering and Management Vaud, HES-SO University of Applied Sciences and Arts Western Switzerland
000015225 773__ $$tInternational Journal of Data Science and Analytics$$j2024
000015225 8564_ $$uhttps://arodes.hes-so.ch/record/15225/files/Levchenko_2024_Chain-structured_neural_architecture.pdf$$yPublished version$$9668f1b76-4d95-4da2-afe2-824e28846312$$s702063
000015225 906__ $$aGOLD
000015225 909CO $$ooai:hesso.tind.io:15225$$pGLOBAL_SET
000015225 950__ $$aaucun
000015225 980__ $$ascientifique
000015225 981__ $$ascientifique