TY - GEN AB - Neural 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. AD - School of Engineering and Management Vaud, HES-SO University of Applied Sciences and Arts Western Switzerland AD - School of Engineering and Management Vaud, HES-SO University of Applied Sciences and Arts Western Switzerland AD - School of Engineering and Management Vaud, HES-SO University of Applied Sciences and Arts Western Switzerland AD - Predictive Layer SA, Rolle, Switzerland AD - School of Engineering and Management Vaud, HES-SO University of Applied Sciences and Arts Western Switzerland AU - Levchenko, Denis AU - Rappos, Efstratios AU - Ataee, Shabnam AU - Nigro, Biagio AU - Robert-Nicoud, Stephan CY - Berlin, Germany DA - 2024-12 DO - 10.1007/s41060-024-00690-y DO - DOI ID - 15225 JF - International Journal of Data Science and Analytics KW - neural architecture search KW - time series forecasting KW - hyperparameter optimization KW - deep learning KW - neural networks KW - reinforcement learning L1 - https://arodes.hes-so.ch/record/15225/files/Levchenko_2024_Chain-structured_neural_architecture.pdf L2 - https://arodes.hes-so.ch/record/15225/files/Levchenko_2024_Chain-structured_neural_architecture.pdf L4 - https://arodes.hes-so.ch/record/15225/files/Levchenko_2024_Chain-structured_neural_architecture.pdf LA - eng LK - https://arodes.hes-so.ch/record/15225/files/Levchenko_2024_Chain-structured_neural_architecture.pdf N2 - Neural 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. PB - Springer Nature PP - Berlin, Germany PY - 2024-12 SN - 2364-415X T1 - Chain-structured neural architecture search for financial time series forecasting TI - Chain-structured neural architecture search for financial time series forecasting UR - https://arodes.hes-so.ch/record/15225/files/Levchenko_2024_Chain-structured_neural_architecture.pdf VL - 2024 Y1 - 2024-12 ER -