## A. General Blind Spots in AI/ML for Storage Systems:

Despite significant progress in applying AI and ML to a wide range of storage system challenges, several critical gaps remain that future research must address.

**Explainability:** The issues of explainability, transparency, and interpretability are rarely addressed in the studies reviewed. The majority of contributions emphasize performance metrics, often neglecting to evaluate or discuss the interpretability of the proposed methods. Although some models employed are inherently interpretable, most recent approaches rely on black-box models, with little consideration given to their explainability. Consequently, when a system exhibits suboptimal or unexpected behavior, it becomes challenging to tune, debug, or analyze such systems, thereby limiting their practical applicability.

This gap is particularly significant for several reasons:

* workloads, systems, and technologies evolve rapidly, requiring storage system solutions to be adaptable and tunable;

* research progress depends on understanding the reasons behind the failure or success of specific techniques, as well as the ability to rigorously compare new solutions to established ones, which requires to have the ability to analyze and understand them;

* most state-of-the-art models are evaluated using common benchmarks and workloads, yet their behavior in production environments can differ substantially, analysis and tuning abilities are required in that case.

We therefore recommend that future research systematically address explainability, both in model development and in the analysis and reporting of experimental results.

**Reproducibility:** Except for studies providing artifacts or accessible code repositories, reproducing experiments remains a significant challenge, even when detailed methodological descriptions are available. This impedes not only verification but also the reuse and meaningful comparison of results across studies. The reproducibility issue is closely tied to explainability: AI-based solutions inherently involve a larger number of variables—such as diverse frameworks, feature sets, and stochastic elements like random parameter initialization—compared to traditional approaches. This is particularly problematic with opaque black-box components, where small changes can lead to significant performance differences. These complexities make faithful replication difficult, thus hindering the widespread adoption and incremental advancement of AI-driven methods in storage system research.

We therefore recommend that researchers share complete artifacts (code, data, configurations), report experimental details (e.g., random seeds, hardware/software setups), and adopt automated, documented workflows.

**AI/ML Overhead:** Despite the growing adoption of AI/ML techniques for resource optimization, the evaluation of associated overheads remains insufficiently addressed in most studies. Overhead analysis is often limited or partial—especially with regard to resource usage during model training. Several studies emphasize performance improvements while overlook-

ing the significant computational and energy costs incurred by developing complex models.

On the inference side, systematic reporting of resource usage—including CPU, memory, and storage consumption—is still rare. Few studies rigorously compare the operational costs of AI models against established approaches, such as simple heuristics, metaheuristics, or conventional optimization strategies. This lack of comprehensive benchmarking makes it difficult to assess whether the deployment of advanced models, like deep reinforcement learning, is truly justified, particularly when simpler feedback control methods could deliver good results with far lower resource consumption and maintenance complexity.

Overall, robust overhead evaluation, including both training and inference phases, should become a standard component of research in this area. Without it, the practical value and efficiency of AI-driven storage optimization remain questionable.

We therefore recommend that researchers systematically report the overheads introduced by AI solutions, detailing both training and inference phases, using a transparent and reproducible methodology.

## B. Category-Specific Challenges and Opportunities:

1) Device level: Despite the increasing adoption of machine learning techniques in SSD optimization, several challenges remain, particularly when considering emerging storage device architectures and novel system integration paradigms.

**Specialized SSD devices.** One major gap is the lack of ML and AI techniques tailored to specialized SSDs, such as ultra-low latency SSDs (e.g., Samsung Z-NAND, Intel Optane) and Zoned Namespace (ZNS) SSDs. Ultra-low latency SSD devices are optimized for extremely low read latencies and demand minimal firmware overhead, which poses significant challenges for deploying ML or AI models that require even modest inference time. Current ML- and AI- based approaches seldom consider the extreme timing sensitivity of these devices, and it remains an open question how to adapt predictive logic to meet sub-microsecond response requirements. Similarly, ZNS SSDs, which enforce sequential write constraints within zones, expose a different set of management challenges such as zone-level garbage collection, allocation, and write coalescing. These are potential areas where ML models could contribute, but most existing studies still focus on traditional page- or block-level FTL management and so there are open venues to explore zone-oriented prediction or policy learning in depth.

**Computational storage devices.** Another underexplored opportunity lies in computational storage devices (CSDs). Unlike traditional SSDs, CSDs integrate processing capabilities directly into the storage device, enabling significantly higher compute capacity for in-situ data processing. This architectural shift allows for the potential deployment of more complex ML models, including deep neural networks, which would be infeasible on conventional SSD controllers due to power, latency, and memory constraints. Furthermore, CSD designs may incorporate dedicated neural network accelerators, such as NPUs or low-power matrix engines, making them well-suited to host advanced ML tasks like predictive prefetching,
access pattern classification, or cross-layer policy coordination. While some studies explored optimizing storage for AI thanks to the embedded computational power (storage for AI is out of the scope of this paper), very few explored how to optimize storage with AI on such devices. Key open questions include how to co-optimize ML inference with I/O scheduling, balance compute and storage bandwidth, and coordinate with host-side intelligence in hybrid systems.

2) *Caching and Prefetching*: As artificial intelligence continues its rapid transformation and disrupts diverse domains within computer science, intelligent caching for storage systems is no exception. Traditional caching challenges—such as dynamic workload adaptation and efficient resource management—are now poised to benefit from recent AI optimizations, including model pruning, quantization, and the deployment of compact, task-specific models. These methods are especially relevant for latency-sensitive, resource-constrained caching environments, where classical solutions may fall short.

We foresee opportunities in the adoption of agentic AI—that is, autonomous, decision-making agents capable of reasoning and collaboration—for distributed storage systems. Deploying multi-agent architectures allows each storage node or cluster to learn and optimize caching strategies locally, while also cooperating across distributed systems. For example, in a content delivery network, AI agents could identify regional access trends and dynamically share cache resources, leading to improved overall system throughput and responsiveness.

A recurring limitation in the field is the siloed approach to AI deployment: most surveyed systems restrict learning models to a single layer, often at the application or storage level, and rarely extend optimization efforts across the entire storage stack. We believe the next wave of research should explore cross-layer learning and system-wide intelligence, fostering models that can identify and act upon patterns spanning from user behavior to hardware constraints. Aggregating models across layers could provide holistic insights and balance competing applications more effectively.

The staleness of training data is another major concern. Many AI-driven caching solutions are still benchmarked on open I/O traces, sometimes outdated, failing to reflect the evolving realities of cloud, edge or HPC environments. It is crucial to move beyond static, off-line learning and invest in adaptive, continually-updating models that maintain accuracy as workloads, deployment topologies, or usage patterns shift. Real-world deployment should prioritize the ability of AI models to self-tune in response to changing system conditions, as well as ensure inference latency and overhead remain suitable for production use.

3) *System-level Optimizations*: AI methods have begun to reshape every layer of the storage stack, yet each of the five optimizations surveyed here still faces gaps that limit robust, production-grade adoption.

**Data Placement** research is dominated by single-agent deep-RL or supervised predictors trained on historical traces. These approaches suffer from sample inefficiency, limited explainability, and privacy concerns when global traces must be centralized. Future work should explore federated or multi-agent learners that cooperate across tiers – or even across

clouds – while respecting data-sovereignty constraints and reducing training cost. Richer cross-layer signals (e.g., power states, network congestion, security labels) could give placement agents a wider context for holistic decisions.

**Data Deduplication** has made impressive strides in semantic similarity detection via deep embeddings, yet index scalability and near-real-time adaptation remain open. No current system offers an online learning loop that updates embeddings as new file types or transformations appear, nor do today's models quantify privacy leakage when chunk vectors are shared for hashing. Lightweight continual-learning or prompt-tuned LLM techniques could provide rapid adaptation without rebuilding large encoders, while privacy-preserving ANN indices (e.g., homomorphic encryption- or differential-privacy-backed) would extend applicability to multi-tenant clouds.

**I/O Scheduling** demonstrates that RL can mitigate bursiteness and QoS violations under partial observability, but most schedulers treat devices as black-boxes and optimizes a single performance metric. Next-generation agents should reason simultaneously about latency, tail distribution, energy, and carbon cost, and coordinate across layers – device firmware, OS queues, and network fabric – via hierarchical or agentic RL. In addition, formally verified safe-RL techniques are needed to bound worst-case latency during exploration phases.

**Storage-system tuning** currently relies on offline black-box regressors or sample-efficient search, yet struggles with configuration drift, multi-objective trade-offs, and heterogeneous tenancy. We anticipate a shift toward self-optimizing “digital twin” frameworks that blend surrogate models, Bayesian optimization, and online causal inference to predict how knob changes ripple through the full I/O path. Integrating cost and sustainability signals directly into the objective – and enabling cooperative tuning across middleware, file-system, and network layers – remains largely unexplored.

Across all four areas, three system shortcomings emerge:

* *Stale or non-representative training data*, with many studies still benchmarking on obsolete or synthetic traces.

* *Isolated, layer-specific optimization* that ignores cross-stack interactions.

* Limited attention to *operational constraints* such as energy budgets, carbon targets, or security compliance.

Addressing these gaps calls for continuous-learning pipelines fed by live telemetry, cross-layer agentic architectures that reason about end-to-end objectives, and principled evaluation frameworks that measure not only performance gains but also overhead, sustainability, and privacy impact.

4) *Workload and performance prediction:* Use of AI in predicting storage system and application data access performance and in characterizing workloads has been effective. There are several open research questions that must be addressed in the future to improve the use of AI. These gaps are in limitations in the accuracy of the models, in the reuse of AI models for different systems or different applications, in adapting the models under interference, and in using them across multiple layers of storage (within a system and among multiple systems). Accuracy of many of the models developed is generally low. While some models
showed high accuracy, they have been trained with data that has been curated to single applications. For instance, the model used by Behzad et al. [173] was following the general trend of the I/O performance. However, accuracy was low. Similarly, a recent work, AIIO from Dong et al. [172] used multiple AI models and selected the best model for each application. Adaptability of models has been limited in predicting I/O performance. The models are good for the same application running on the same compute system. However, the models are not portable for other applications or for other systems. Availability of logs that are compatible on different systems is also a challenge. While some systems use profiling tools such as Darshan automatically, there are not many systems that collect these logs. The coverage of the collection of the logs and their availability is limited, hindering the development of high-performance AI models. In addition, automating the execution of optimization decisions based on the AI training models is still in its inception. While there has been some improvement of predicting performance bottlenecks and their root causes, identification of tuning approaches is mostly human effort. Moreover, the timing for applying the tuning is uncertain because some applications may need code changes before they can take advantage of the optimizations. Considering the limited accuracy, portability, and autonomy, there are several research gaps in workload characterization, performance prediction, and performance tuning.

5) *Anomalies detection:* As mentioned in RQ2 of Section X-D, only a minority of works surveyed in this category explicitly cover the integration and deployment of AI models in storage systems and conduct measurements on real systems at runtime, which suggests the challenges and opportunities for further improvements on system integration. In terms of AI techniques, as shown in Table VII, a wide spectrum of AI techniques ranging from classic LiR [191], [194] and SVM [191], [194] to reinforced learning (e.g., [208], [210]) and deep learning (e.g., [201], [202], [207], [209], [52]) have been explored for HDD/SSD failure predictions. On the other hand, a much less number of AI techniques have been applied for system-level anomalies, and many explored techniques are relatively traditional (e.g., PCA [28], [218], DBSCAN [28]), which may suggest the challenge and/or opportunity for applying the diverse AI techniques in disk failure prediction to address system-level issues. In terms of dataset, we notice that the public Backblaze dataset has enabled lots of research on disk failure prediction (e.g., [35], [191], [192], [36], [37], [195], [196], [197], [198], [199], [204]), but the dataset at the system levels are relatively scarce. For example, the unavailability of public RAID error logs severely limits follow-up research at this level. Given the critical importance of representative logs for failure prediction, we call for the collective efforts from the community to address the open challenge of the availability of logs from production storage systems.

## XII. SHORT SUMMARY

Given the rapid evolution of AI/ML, the storage systems community is actively investigating their potential for optimization; nevertheless, the pace of advancement has hindered

the consolidation of approaches, resulting in fragmented efforts, a lack of a global perspective, and persistent blind spots in current research. This paper aims to provide a comprehensive overview of the state of the art in using AI/ML in the design, management, and optimization of storage systems in different domains. We tried to classify different studies, discuss the literature through several research questions (RQs), identify some gaps, formulate guidelines, and highlight some future challenges.

This paper adopts a hybrid organizational structure, arranged both by layer (device, system, and application/workload) and by function (e.g., caching and anomaly detection). A strictly layer-based or function-based structure proved inadequate, as the literature shows uneven exploration across layers and functions, with certain functions spanning multiple layers and requiring highly context-specific optimizations. The chosen hybrid approach is intended to provide a clearer and more coherent narrative, making it easier for the reader to follow.

We first examined what makes AI/ML techniques particularly compelling for storage systems and how data-driven models can address challenges at different levels of the storage stack (RQ1). We then analyzed how AI/ML models have been deployed and how their associated costs have been discussed—albeit in a limited manner, with several critical facets either omitted or insufficiently examined (RQ2). With respect to adaptability, we highlighted the lack of attention to explainability and reproducibility in many studies, which poses a barrier to sustained research progress in the field (RQ3). Furthermore, as AI/ML methods require substantial amounts of training data, prior work has largely relied on publicly available traces and workloads—often outdated—thereby overlooking important issues of data privacy and security.

Exploring AI/ML techniques is of great importance, and it is essential for the storage systems community to continue investigating these approaches to optimize performance, energy efficiency, and other QoS metrics. At the same time, fundamental research principles must be upheld to ensure that proposed techniques are reproducible, reusable, and explainable, thereby enabling incremental progress. Moreover, the community must critically examine questions surrounding data—specifically its selection, availability, and suitability for experimentation—while addressing concerns of privacy, security, and the relevance of models trained on such data.

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<figure><img src="image_5.png" /></figure>

**Jalil Boukhobza** received the electrical engineering (with Hons.) degree from the Institut National d'Electricite et d'electronique (I.N.E.L.E.C) Boumerdes, Algeria, in 1999, and the MSc and PhD degrees in computer science from the University of Versailles, France, in 2000 and 2004, respectively. He is a professor with the ENSTA, Institut Polytechnique de Paris, a French State Graduate, Post-Graduate and Research Institute. He was a research fellow with the PRiSM Laboratory (University of Versailles) from 2004 to 2006. He was an associate

professor with the University Bretagne Occidentale, Brest, France, from 2006 to 2020 and is a member of Lab-STICC. He has also been working with the Technology Research Institute (IRT) bcom since 2013. His main research interests include storage system design, performance evaluation and energy optimization, and operating system design. He works on different application domains such as embedded systems, cloud computing, and database systems.

<figure><img src="image_6.png" /></figure>

**Suren Byna** is a Professor in the Department of Computer Science and Engineering (CSE) at The Ohio State University (OSU). He was a Senior Computer Scientist at Lawrence Berkeley National Laboratory (LBNL), where he is now a Visiting Faculty Scientist. He leads the Innovative Data Technologies Lab at OSU. His research interests span across many topics in scientific data management. These topics include parallel file systems, next-generation data systems, I/O libraries, file formats, data compression strategies, data movement performance characterization

and tuning, performance data visualization, efficient scientific workflows, scientific metadata and provenance management strategies, energy-aware computing, and heterogeneous computing. He leads projects in the areas of novel data management systems, data quality, cybersecurity and trustworthiness of data, data readiness, and agentic AI for data management.

<figure><img src="image_7.png" /></figure>

**Yuan-Hao Chang** (SM'14–F'23) received his Ph.D. in Computer Science from the Department of Computer Science and Information Engineering at National Taiwan University, Taipei, Taiwan. He is currently a Professor of Department of Computer Science and Information Engineering, National Taiwan University. His research interests include memory/storage systems, operating systems, embedded systems, and real-time systems. He is an IEEE Fellow.

<figure><img src="image_8.png" /></figure>

**Shadi Ibrahim** is a Research Scientist at Inria. He obtained his Ph.D. degree in Computer Science from Huazhong University of Science and Technology (HUST) in Wuhan of China in 2011. His current research interests are in scalable data management, storage systems, data-intensive computing, parallel I/O and distributed file systems, virtualization technology and Cloud/Fog computing. His work has been published in prestigious international journals and conferences in distributed and parallel systems (IEEE TPDS, ACM Computing Surveys, Elsevier

FGCS, SoCC, ICDCS, IPDPS, VEE, CCGrid, and Cluster). He is the recipient of the IEEE TCSC Award for Excellence in Scalable Computing (Middle Career Researcher) in 2020 and currently serves on the Editorial Board of IEEE Internet Computing magazine and IEEE Transactions on Cloud Computing. He is a Distinguished member of the ACM and a Senior member of IEEE.
<figure><img src="image_9.png" /></figure>

**Youngjae Kim** (Member, IEEE) received the BS degree in computer science from Sogang University, South Korea, in 2001, the MS degree in computer science from KAIST, in 2003, and the PhD degree in computer science and engineering from Pennsylvania State University, University Park, Pennsylvania, in 2009. He is currently a professor with the Department of Computer Science and Engineering, Sogang University, Seoul, South Korea. Before joining Sogang University, Seoul, South Korea, he was a R&D staff scientist with the US Department of Energy's

Oak Ridge National Laboratory (2009-2015) and as an assistant professor at Ajou University, Suwon, South Korea (2015-2016). His research interests include operating systems, file and storage systems, parallel and distributed systems, computer systems security, and performance evaluation.

<figure><img src="image_10.png" /></figure>

**Amelie Chi Zhou** is currently an Assistant Professor in Hong Kong Baptist University (HKBU). Before joining HKBU, she was a faculty member in Shenzen University and a Postdoc Fellow in INRIA, France. She received her PhD degree in 2016 from Nanyang Technological University, Singapore. Her research interests lie in high performance computing, big data processing and resource management. She is a recipient of the IEEE-CS TCHPC Early Career Award and the ACM SIGHPC China Rising Star Award. She serves on the editorial board of IEEE

Transactions on Parallel and Distributed Systems, Journal of Parallel and Distributed Computing, and Future Generation Computer Systems.

<figure><img src="image_11.png" /></figure>

**Safdar Jamil** received the BE degree in Computer Systems Engineering from Mehran University of Engineering and Technology (MUET), Jamshoro, Pakistan in 2017. He is working toward the MS leading to PhD integrated program degree in the Department of Computer Science and Engineering, Sogang University, Seoul, South Korea. Currently, he is a member in Data-Intensive Computing & Systems Laboratory with the Department of Computer Science and Engineering, Sogang University, Seoul, South Korea. His research interests include scalable

indexing data structures and algorithms, key-value stores, Zoned Namespace Storage, and Storage Optimization Techniques.

<figure><img src="image_12.png" /></figure>

**Chieh-Lin Tsai** received his M.S. degree from the Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan, in 2017. He previously worked as an IT engineer at Taiwan Semiconductor Manufacturing Company (TSMC). He is currently pursuing the Ph.D. degree in the Department of Computer Science and Information Engineering, National Taiwan University. His primary research interests include non-volatile memory systems and system-level design for in-memory and in-storage computing.

<figure><img src="image_13.png" /></figure>

**Mai Zheng** is an Associate Professor in the Department of Electrical and Computer Engineering at Iowa State University. He leads the Data Storage Lab to investigate problems in critical systems that compromise data integrity and other key properties. His work has appeared at storage systems venues (e.g., FAST, MSST, SYSTOR, TOS, HotStorage), parallel computing venues (e.g., HPDC, ICS, IPDPS, PPoPP), and other systems venues (e.g., OSDI, ASPLOS, ATC, TOCS). He is a recipient of the National

Science Foundation (NSF) CAREER Award, the Samsung Global Research Outreach (GRO) Award, and a few Best Paper Honorable Mentions at FAST and HotStorage. He has served as an Associate Editor of ACM Transactions on Storage (TOS), a General Chair of ACM HotStorage, and a Program Committee member of FAST, ATC, SC, etc. Mai received his Ph.D. degree in Computer Science from The Ohio State University in 2015.
APPENDIX A
LIST OF ACRONYMS

Table VIII presents acronyms used throughout this paper.
<table><caption>TABLE VIII
ACRONYMS USED IN THIS PAPER.</caption><thead><tr><td>Acronym</td><td>Expanded Form</td></tr></thead><tbody><tr><td>AE</td><td>Auto-Encoder</td></tr><tr><td>AI</td><td>Artificial Intelligence</td></tr><tr><td>AIIO</td><td>AI for IO</td></tr><tr><td>ANN</td><td>Artificial Neural Network</td></tr><tr><td>BART</td><td>Bayesian Additive Regression Trees</td></tr><tr><td>BiLSTM</td><td>Bidirectional Long Short-Term Memory</td></tr><tr><td>CART</td><td>Classification and Regression Trees</td></tr><tr><td>CGP</td><td>Categorical Gaussian Process</td></tr><tr><td>CNN</td><td>Convolutional Neural Networks</td></tr><tr><td>DBSCAN</td><td>Density-Based Spatial Clustering of Applications with Noise</td></tr><tr><td>DDPG</td><td>Deep Deterministic Policy Gradient</td></tr><tr><td>DFT</td><td>Discrete Fourier Transform</td></tr><tr><td>DL</td><td>Deep Learning</td></tr><tr><td>DQN</td><td>Deep Q-Networks</td></tr><tr><td>DRL</td><td>Deep Reinforcement Learning</td></tr><tr><td>DT</td><td>Decision Trees</td></tr><tr><td>FFN</td><td>Feed Forward Neural Network</td></tr><tr><td>FL</td><td>Federated Learning</td></tr><tr><td>GA</td><td>Genetic Algorithms</td></tr><tr><td>GAN</td><td>Generative Adversarial Networks</td></tr><tr><td>GBDT</td><td>Gradient Boosted Decision Trees</td></tr><tr><td>GBM</td><td>Gradient Boosting Machines</td></tr><tr><td>GBRT</td><td>Gradient Boosted Regression Trees</td></tr><tr><td>GCN</td><td>Graph Convolutional Network</td></tr><tr><td>GLM</td><td>Gamma Linear Model</td></tr><tr><td>GMM</td><td>Gaussian Mixture Model</td></tr><tr><td>GNN</td><td>Graph Neural Network</td></tr><tr><td>GP</td><td>Gaussian Process</td></tr><tr><td>GRU</td><td>Gated Recurrent Unit</td></tr><tr><td>HDBSCAN</td><td>Hierarchical Density-Based Spatial Clustering of Applications with Noise</td></tr><tr><td>IF</td><td>Isolation Forest</td></tr><tr><td>ION</td><td>IO Navigator</td></tr><tr><td>KNN</td><td>K-nearest Neighbors (algorithm)</td></tr><tr><td>LightGBM</td><td>Light Gradient Boosting Machine</td></tr><tr><td>LR</td><td>Logistic Regression</td></tr><tr><td>LiR</td><td>Linear Regression</td></tr><tr><td>LLM</td><td>Large Language Model</td></tr><tr><td>LM</td><td>Linear Model</td></tr><tr><td>LMT</td><td>Lustre Monitoring Toolkit</td></tr><tr><td>LOF</td><td>Local Outlier Factor</td></tr><tr><td>LoR</td><td>Logistic Regression</td></tr><tr><td>LP</td><td>Label Propagation</td></tr><tr><td>LSTM</td><td>Long Short-Term Memory</td></tr><tr><td>MARS</td><td>Multivariate Adaptive Regression Splines</td></tr><tr><td>MDP</td><td>Markov Decision Processes</td></tr><tr><td>ML</td><td>Machine Learning</td></tr><tr><td>MLP</td><td>Multilayer Perceptron</td></tr><tr><td>NN</td><td>Neural Networks</td></tr><tr><td>NLP</td><td>Natural Language Processing</td></tr><tr><td>NRMSE</td><td>Normalized Root-mean-square error</td></tr><tr><td>ORF</td><td>Online Random Forests</td></tr><tr><td>PCA</td><td>Principal Component Analysis</td></tr><tr><td>PPO</td><td>Proximal Policy Optimization</td></tr><tr><td>PGM</td><td>Probablistic Graphical Model</td></tr><tr><td>RAG</td><td>Retrieval-Augmented Generation</td></tr><tr><td>RF</td><td>Random Forests</td></tr><tr><td>RGF</td><td>Regularized Greedy Forests</td></tr><tr><td>RL</td><td>Reinforcement Learning</td></tr><tr><td>RMSE</td><td>Root Mean Square Error</td></tr><tr><td>RNN</td><td>Recurrent Neural Networks</td></tr><tr><td>RoTI</td><td>Return on Tuning Investment</td></tr><tr><td>SARSA</td><td>State-Action-Reward-State-Action</td></tr><tr><td>S-CDA</td><td>Smart Cloud Disk Allocation</td></tr><tr><td>SHAP</td><td>SHapley Additive exPlanations</td></tr><tr><td>STSNN</td><td>Spatial-Temporal-Sequential Neural Network</td></tr><tr><td>SVM</td><td>Support Vector Machines</td></tr><tr><td>TabNet</td><td>Attentive Interpretable Tabular Learning</td></tr><tr><td>TCN</td><td>Temporal Convolutional Network</td></tr><tr><td>TGP</td><td>Treed Gaussian Processes</td></tr><tr><td>VAE</td><td>Variational Auto-Encoder</td></tr><tr><td>VM</td><td>Virtual Machine</td></tr><tr><td>XGBoost</td><td>Extreme Gradient Boosting</td></tr></tbody></table>