by jsendak | Dec 29, 2023 | Computer Science
In this article, we delve into the challenging task of accurately forecasting financial returns, specifically focusing on cryptocurrency returns. Cryptocurrency markets are known for their chaotic and complex nature, making the prediction process even more arduous. However, using a novel prediction algorithm rooted in the Hawkes model and limit order book (LOB) data, we present a precise forecast of return signs. By leveraging predictions of future financial interactions and capitalizing on the non-uniformly sampled structure of the original time series, our approach outperforms benchmark models in both prediction accuracy and cumulative profit in a trading environment. To prove the efficacy of our approach, we conduct Monte Carlo simulations across 50 scenarios utilizing LOB measurements from a centralized cryptocurrency exchange involving the stablecoin Tether and the U.S. dollar.
Abstract:Accurately forecasting the direction of financial returns poses a formidable challenge, given the inherent unpredictability of financial time series. The task becomes even more arduous when applied to cryptocurrency returns, given the chaotic and intricately complex nature of crypto markets. In this study, we present a novel prediction algorithm using limit order book (LOB) data rooted in the Hawkes model, a category of point processes. Coupled with a continuous output error (COE) model, our approach offers a precise forecast of return signs by leveraging predictions of future financial interactions. Capitalizing on the non-uniformly sampled structure of the original time series, our strategy surpasses benchmark models in both prediction accuracy and cumulative profit when implemented in a trading environment. The efficacy of our approach is validated through Monte Carlo simulations across 50 scenarios. The research draws on LOB measurements from a centralized cryptocurrency exchange where the stablecoin Tether is exchanged against the U.S. dollar.
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by jsendak | Dec 29, 2023 | Computer Science
We present OpenRL, an advanced reinforcement learning (RL) framework designed to accommodate a diverse array of tasks, from single-agent challenges to complex multi-agent systems. OpenRL’s robust support for self-play training empowers agents to develop advanced strategies in competitive settings.
Notably, OpenRL integrates Natural Language Processing (NLP) with RL, enabling researchers to address a combination of RL training and language-centric tasks effectively. Leveraging PyTorch’s robust capabilities, OpenRL exemplifies modularity and a user-centric approach.
It offers a universal interface that simplifies the user experience for beginners while maintaining the flexibility experts require for innovation and algorithm development. This equilibrium enhances the framework’s practicality, adaptability, and scalability, establishing a new standard in RL research.
To delve into OpenRL’s features, we invite researchers and enthusiasts to explore our GitHub repository at this https://github.com/OpenRL and access our comprehensive documentation at this https://docs.openrl.org.
Abstract:We present OpenRL, an advanced reinforcement learning (RL) framework designed to accommodate a diverse array of tasks, from single-agent challenges to complex multi-agent systems. OpenRL’s robust support for self-play training empowers agents to develop advanced strategies in competitive settings. Notably, OpenRL integrates Natural Language Processing (NLP) with RL, enabling researchers to address a combination of RL training and language-centric tasks effectively. Leveraging PyTorch’s robust capabilities, OpenRL exemplifies modularity and a user-centric approach. It offers a universal interface that simplifies the user experience for beginners while maintaining the flexibility experts require for innovation and algorithm development. This equilibrium enhances the framework’s practicality, adaptability, and scalability, establishing a new standard in RL research. To delve into OpenRL’s features, we invite researchers and enthusiasts to explore our GitHub repository at this https URL and access our comprehensive documentation at this https URL.
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by jsendak | Dec 29, 2023 | Computer Science
The size and complexity of machine learning (ML) models have grown rapidly in recent years. However, the methods for evaluating their performance have not kept pace with this progress. The ML community continues to rely on traditional performance metrics, such as the area under the receiver operating characteristic curve (AUROC) and sensitivity/specificity measures, to assess model performance.
In this article, we argue that relying solely on these metrics provides only a limited understanding of how a model performs and its ability to generalize. We propose that considering scores derived from the test receiver operating characteristic (ROC) curve alone is insufficient and fails to capture the full range of a model’s performance.
We explore alternative approaches for assessing ML model performance and highlight the limitations of relying solely on AUROC and sensitivity/specificity measures. We suggest that incorporating additional metrics, such as precision, recall, and F1 score, can provide a more comprehensive evaluation of model performance.
By broadening our perspective and adopting a multi-metric approach, we can gain deeper insights into the strengths and weaknesses of ML models. This will enable us to make more informed decisions about model deployment and improve the overall reliability and generalizability of ML systems.
The Need for Modern Performance Assessment
As ML models become increasingly complex, relying on traditional performance metrics alone is no longer sufficient. The AUROC and sensitivity/specificity measures fail to capture important aspects of a model’s performance, such as its ability to handle imbalanced datasets and its robustness to different threshold values.
Moreover, focusing solely on the test ROC curve neglects the valuable information provided by the validation ROC curve. By considering both curves, we can better understand how a model generalizes to unseen data and identify potential overfitting or underfitting issues.
Exploring Alternative Metrics
We propose incorporating additional metrics, such as precision, recall, and the F1 score, into the performance assessment of ML models. These metrics provide a more detailed evaluation of model performance, capturing factors such as the trade-off between false positives and false negatives.
By considering a range of metrics, we can gain a more nuanced understanding of a model’s strengths and weaknesses. This enables us to make more informed decisions about model selection and fine-tuning, ultimately improving the overall reliability and usefulness of ML systems.
Conclusion
The ML community must move beyond relying solely on AUROC and sensitivity/specificity measures for performance assessment. By considering scores derived from both the test and validation ROC curves, as well as incorporating additional metrics like precision, recall, and the F1 score, we can obtain a more comprehensive understanding of how a model performs and its ability to generalize. This will pave the way for more effective and reliable ML systems in the future.
Abstract:Whilst the size and complexity of ML models have rapidly and significantly increased over the past decade, the methods for assessing their performance have not kept pace. In particular, among the many potential performance metrics, the ML community stubbornly continues to use (a) the area under the receiver operating characteristic curve (AUROC) for a validation and test cohort (distinct from training data) or (b) the sensitivity and specificity for the test data at an optimal threshold determined from the validation ROC. However, we argue that considering scores derived from the test ROC curve alone gives only a narrow insight into how a model performs and its ability to generalise.
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by jsendak | Dec 29, 2023 | Computer Science
In this article, we explore the concept of AIXI, a Bayesian optimality notion for general reinforcement learning. Previous approaches to approximating AIXI’s Bayesian environment model relied on a predefined set of models, introducing uncertainty for the agent. We address this issue in the context of Human-AI teaming, introducing a new agent called DynamicHedgeAIXI. This agent maintains an exact Bayesian mixture over dynamically changing sets of models and offers strong performance guarantees. Through experiments on epidemic control on contact networks, we validate the practical utility of the DynamicHedgeAIXI agent.
Abstract:Prior approximations of AIXI, a Bayesian optimality notion for general reinforcement learning, can only approximate AIXI’s Bayesian environment model using an a-priori defined set of models. This is a fundamental source of epistemic uncertainty for the agent in settings where the existence of systematic bias in the predefined model class cannot be resolved by simply collecting more data from the environment. We address this issue in the context of Human-AI teaming by considering a setup where additional knowledge for the agent in the form of new candidate models arrives from a human operator in an online fashion. We introduce a new agent called DynamicHedgeAIXI that maintains an exact Bayesian mixture over dynamically changing sets of models via a time-adaptive prior constructed from a variant of the Hedge algorithm. The DynamicHedgeAIXI agent is the richest direct approximation of AIXI known to date and comes with good performance guarantees. Experimental results on epidemic control on contact networks validates the agent’s practical utility.
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by jsendak | Dec 29, 2023 | Computer Science
Analysis of LightGCN in Graph Recommendation Algorithms
In this article, we delve into the core themes of LightGCN in the context of graph recommendation algorithms. While originally designed for graph classification, LightGCN introduces a linear propagation approach for embeddings that proves to enhance performance. We replicate the original findings, investigate the robustness of LightGCN across various datasets and metrics, and also explore the potential of using Graph Diffusion as a means of augmenting signal propagation in LightGCN.
Abstract:This paper analyses LightGCN in the context of graph recommendation algorithms. Despite the initial design of Graph Convolutional Networks for graph classification, the non-linear operations are not always essential. LightGCN enables linear propagation of embeddings, enhancing performance. We reproduce the original findings, assess LightGCN’s robustness on diverse datasets and metrics, and explore Graph Diffusion as an augmentation of signal propagation in LightGCN.
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