Paper: arXiv 2403.14695

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper involves advanced hyperparameter optimization techniques and neural network architecture search, but focuses on practical implementation with real-world financial datasets, backtesting, and specific performance metrics like F1 and AUC.

Research Flowchart

  flowchart TD
  A["Research Goal: Optimize Neural Networks<br>for Financial Time Series Forecasting"] --> B["Methodology: Chain-Structured NAS<br>with 3 Strategies & 5 Architectures"]
  B --> C["Data Inputs: Financial Datasets<br>(High Variance, Small Size)"]
  C --> D{"Computational Process"}
  D --> E["Bayesian Optimization<br>(Tree-structured Parzen Estimator)"]
  D --> F["Hyperband Method<br>(Resource-based Pruning)"]
  D --> G["Reinforcement Learning<br>(Policy Gradient)"]
  E & F & G --> H["Key Outcomes"]
  H --> I["Best Strategies: Bayesian & Hyperband"]
  H --> J["Best Architectures: RNN & 1D CNN"]

Cited References (88)

  1. CNNpred: CNN-based stock market prediction using a diverse set of variables (2019) — link
  2. Neural Architecture Search: A Survey (2019) — link
  3. Neural Architecture Search with Reinforcement Learning (2017)
  4. Algorithms for Hyper-Parameter Optimization (2011) — link
  5. Practical Bayesian Optimization of Machine Learning Algorithms (2012) — link
  6. Gaussian processes for machine learning. (2006)
  7. Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016) — link
  8. A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning (2010)
and 80 more
  1. Towards an Empirical Foundation for Assessing Bayesian Optimization of Hyperparameters (2013) — link
  2. Optuna: A Next-generation Hyperparameter Optimization Framework (2019)
  3. KerasTuner (2019) — link
  4. Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization (2018)
  5. Automated Machine Learning in Action (2022)
  6. Human-level control through deep reinforcement learning (2015)
  7. Q-learning (1992)
  8. On-line Q-learning using connectionist systems (1994)
  9. Mastering the game of Go with deep neural networks and tree search (2016)
  10. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play (2018)
  11. Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates (2017)
  12. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor (2018)
  13. Informing sequential clinical decision-making through reinforcement learning: an empirical study (2011)
  14. Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation (2018)
  15. Extremely randomized trees (2006)
  16. Deep reinforcement learning with double q-learning (2016)
  17. Addressing function approximation error in actor-critic methods (2018)
  18. The sharpe ratio (1998)
  19. Principal component analysis (1987)
  20. Long short-term memory (1997)
  21. Temporal Fusion Transformers for interpretable multi-horizon time series forecasting (2021) — link
  22. Deep reinforcement learning that matters (2018)
  23. Sunrise: A simple unified framework for ensemble learning in deep reinforcement learning (2021)
  24. A distributional perspective on reinforcement learning (2017)
  25. Implicit quantile networks for distributional reinforcement learning (2018)
  26. On calibration of modern neural networks (2017)
  27. Neural Architecture Search: Insights from 1000 papers (2023) — link
  28. The Transformer Network for the Traveling Salesman Problem (2021) — link
  29. Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman Problem (2022) — link
  30. Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problem (2023) — link
  31. A Lightweight CNN-Transformer Model for Learning Traveling Salesman Problems (2023) — link
  32. Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problems (2023) — link
  33. Solving Quadratic Traveling Salesman Problem with Deep Reinforcement Learning (2022) — link
  34. Learning Heuristics for the TSP by Policy Gradient (2018)
  35. Revised Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks (2018) — link
  36. Solving the Quadratic Assignment Problem using Deep Reinforcement Learning (2023) — link
  37. RL 4 CO : a Unified Reinforcement Learning for Combinatorial Optimization Library (2023) — link
  38. Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem (2024) — link
  39. Accelerating Quadratic Optimization with Reinforcement Learning (2021) — link
  40. Pointer Networks (2015) — link
  41. Neural Combinatorial Optimization with Reinforcement Learning (2017) — link
  42. Unsupervised Machine Learning for the Quadratic Assignment Problem (2023) — link
  43. PyTorch Forecasting (2023) — link
  44. Deep neural networks for quantum circuit mapping (2021) — link
  45. neural-architecture-search (2018)
  46. Reinforcement Learning for Solving the Vehicle Routing Problem (2018) — link
  47. Vehicle Routing Problem Using Reinforcement Learning: Recent Advancements (2022)
  48. The Traveling Salesman Problem: A Computational Study (2006)
  49. Proximal Policy Optimization Algorithms (2017) — link
  50. POPMUSIC for the Travelling Salesman Problem (2019) — link
  51. A Linearithmic Heuristic for the Travelling Salesman Problem (2022) — link
  52. \textbf Design of Heuristic Algorithms for Hard Optimization (2023) — link
  53. On the Emerging Potential of Quantum Annealing Hardware for Combinatorial Optimization (2022) — link
  54. MQT QMAP: Efficient Quantum Circuit Mapping (2023) — link
  55. Machine Learning Optimization of Quantum Circuit Layouts (2023) — link
  56. Optimization of quantum circuit mapping using gate transformation and commutation (2020) — link
  57. A dataset for quantum circuit mapping (2021) — link
  58. The complexity of quantum circuit mapping with fixed parameters (2022) — link
  59. A Dynamic Look-Ahead Heuristic for the Qubit Mapping Problem of NISQ Computers (2020) — link
  60. Reinforcement Learning and DEAR Framework for Solving the Qubit Mapping Problem (2022)
  61. Resource Bounds for Quantum Circuit Mapping via Quantum Circuit Complexity (2024) — link
  62. The symmetric quadratic traveling salesman problem (2013) — link
  63. Exact algorithms and heuristics for the Quadratic Traveling Salesman Problem with an application in bioinformatics (2014) — link
  64. A Polyhedral Study of the Quadratic Traveling Salesman Problem (2016)
  65. An Efficient Hybrid Genetic Algorithm for the Quadratic Traveling Salesman Problem (2023) — link
  66. Algorithms and Experimental Study for the Traveling Salesman Problem of Second Order (2008)
  67. A class of exponential neighbourhoods for the quadratic travelling salesman problem (2020) — link
  68. Geometric and LP-based heuristics for angular travelling salesman problems in the plane (2019) — link
  69. Nearest Neighbor Transformation of Quantum Circuits in 2D Architecture (2020) — link
  70. Machine Learning into Metaheuristics: A Survey and Taxonomy (2021) — link
  71. Machine learning enhancing metaheuristics: a systematic review (2023) — link
  72. The ant system: Optimization by a colony of cooperating agents (1996)
  73. Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance (2023) — link
  74. Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data (2017) — link
  75. Neural Predictor for Neural Architecture Search (2020)
  76. Neural Architecture Search without Training (2021) — link
  77. Deep learning with long short-term memory networks for financial market predictions (2018) — link
  78. Classification-based financial markets prediction using deep neural networks (2017) — link
  79. Evolving Deep Convolutional Neural Networks for Image Classification (2017) — link
  80. Pattern Recognition and Machine Learning (Information Science and Statistics) (2006)

0 of 88 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.