Chain-Structured Neural Architecture Search for Financial Time Series Forecasting

Core Concepts

Auto-ML techniques automate neural network architecture search for financial time series forecasting.

Abstract

The content discusses the comparison of three popular neural architecture search strategies - Bayesian optimization, hyperband method, and reinforcement learning - in the context of financial time series forecasting. It explores the challenges, data preparation, architecture types (feedforward networks, CNNs, RNNs), search spaces, and performance estimation strategies. Results show LSTM and 1D CNN outperforming FFNN with hyperband and Bayesian optimization yielding better results than reinforcement learning. The study highlights the difficulties in predicting financial markets and the impact of random seed variance on model performance.

Stats

"The LSTM with parameters selected by the hyperband method applied to the unseen test data achieved an AUC score of 0.56 on average over 50 test runs."
"For the Japan dataset, the best performing architecture was a 1D CNN coming from Bayesian optimization, achieving an AUC score of 0.54 ± 0.03 over 50 test runs."
"Although tuned 1d CNNs gave an AUC score of 0.6 ± 0.02 on validation data (not used for training) in our repeated testing, on average no architecture could achieve AUC over 0.5 on the test dataset."

Quotes

"The hierarchical structure of neural networks extracts important features automatically."
"Bayesian optimization efficiently explores search space intelligently guiding optimization process."
"Reinforcement learning treats neural architecture search as a reinforcement problem."

Key Insights Distilled From

Chain-structured neural architecture search for financial time series forecasting

by Denis Levche... at arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.14695.pdf

Deeper Inquiries

How can neural architecture search strategies be adapted for non-financial time-series datasets?

In adapting neural architecture search (NAS) strategies for non-financial time-series datasets, it is essential to consider the unique characteristics of the data. One approach is to focus on exploring different types of architectures that are specifically tailored to handle temporal dependencies and patterns present in the dataset. For instance, utilizing recurrent neural networks (RNNs) or convolutional neural networks (CNNs) can be effective in capturing sequential information and spatial features respectively.
Moreover, when dealing with non-financial time-series data, it is crucial to define appropriate evaluation metrics that align with the specific forecasting task at hand. This may involve considering metrics such as mean squared error (MSE), root mean square error (RMSE), or other domain-specific metrics relevant to the dataset being analyzed.
Additionally, incorporating diverse search spaces and strategies within NAS frameworks can help in identifying optimal network configurations for non-financial time-series datasets. By exploring a variety of architectural designs and hyperparameters through techniques like Bayesian optimization, reinforcement learning, or hyperband methods, researchers can efficiently navigate through complex model spaces to find architectures that best suit the data characteristics.

What are the implications of random seed variance on model performance in financial forecasting?

Random seed variance plays a significant role in influencing model performance in financial forecasting tasks. In scenarios where multiple runs of a neural network model with different random seeds result in varying outcomes, it introduces instability and uncertainty into the training process. This variability can lead to challenges in reliably assessing model effectiveness and generalization across unseen data.
In financial forecasting specifically, where even marginal improvements over random predictions hold value, random seed variance poses a critical issue. The fluctuating performance levels due to different initializations make it challenging to determine whether observed gains are truly indicative of improved predictive capabilities or simply artifacts of randomness.
To address these implications effectively, researchers often resort to averaging results over multiple runs with distinct random seeds. By conducting numerous iterations and aggregating outcomes from various initializations, they aim to mitigate the impact of random fluctuations on overall model assessment and decision-making processes within financial applications.

How can ensemble models mitigate variability in neural network predictions?

Ensemble models offer a robust solution for mitigating variability inherent in individual neural network predictions by leveraging diversity among multiple models' outputs. Through combining forecasts from several base models trained on either varied subsets of data or using distinct algorithms/hyperparameters configurations,...
By aggregating predictions from diverse sources—each potentially affected by different instances of randomness—ensemble models tend...
Furthermore,...ensemble methods provide an effective means...to enhance prediction accuracy while reducing sensitivity...to idiosyncrasies associated with single-model approaches....
Overall,...the utilization...of ensemble modeling serves as a powerful strategy...for stabilizing predictions,...improving robustness,...and enhancing overall forecast quality....