Time series forecasting (TSF) is a prominent chal-lenge in data analytics, relevant to both scientific research and real-world industrial applications. The rapid increase in high-dimensional time series data has led researchers to develop numerous models capable of handling complex forecasting tasks across diverse scenarios. Nevertheless, selecting an appropriate model and optimizing its parameters-an issue known as the Combined Algorithm Selection and Hyperparameter optimization (CASH) problem-remains a significant challenge. It is worth investigating how to satisfy both accuracy and efficiency in selecting an optimal algorithm and its hyperparameter configu-ration for a given time series with minimal human intervention. Unfortunately, there is no such work in the field of TSF, which has been developed for more than a decade. Existing methods suffer low selection rate of optimal algorithms. Meanwhile, the TSF task is extremely algorithm-sensitive, and the prediction performance of different algorithms and hyperparameter settings on the same data varies greatly. In this paper, we propose a Proxy-Model-based meta-learning TSF-CASH approach named Auto- Tsf. In the offline training phase, Auto- Tsfextracts the historical experience based on the proxy models, which is used to guide the automatic algorithm selection in the online working phase. The historical experience extracted in the offline phase not only significantly reduces the time consumption for algorithm selection, but also the introduction of the proxy model enhances the optimal algorithm selection rate. Moreover, we propose an asynchronous parallel HPO method in the most time-consuming HPO stage, which further improves the efficiency of the whole TSF -CASH. The experimental results demonstrate that Auto- Tsfachieves SOTA in terms of performance and efficiency compared to existing CASH methods.

In this era of exploding data volumes, more and more complex data analysis tasks are now accomplished by machine learning (ML) or deep learning (DL). Despite the powerful and flexible task processing capability, it also brings challenges such as how to reasonably design the optimal hyperparameters configuration and the huge consumption of time for a single validation. Existing Hyperrarameter Optimization (HPO) methods are gradually facing performance bottlenecks. However, in an era where progressively data-centric big data analytics methods are prevalent, such efficiency issues need to be urgently addressed in the design of intelligent DBMS. In this paper, we propose ShrinkHPO, an efficient and explainable-designed HPO approach with a major focus on ($a$) efficient hyperparameter configuration search strategy, (b) asynchronous executing intervention, and (c) XAI (eXplainable AI) design. ShrinkHPO employs a hyperparameter weight estimation strategy named Shrink-search together with an asynchronous execution design to improve HPO efficiency. To the best of our knowledge, ShrinkHPO is the first HPO method that introduces explainable analysis and verification to ensure the judgment of “high significance” hyperparameters. We also conduct a series of experiments on data analysis tasks in the database domain (classification, regression, time series classification, CASH [1], etc.), collecting HPO results on the usual ML or DL models in each task. Compared to SOTA asynchronous and sequential HPO baselines, ShrinkHPO achieves top performance on both accuracy (RMSE for regression tasks) and time cost, accelerating from 20% to a maximum of 2.2 x.

With the explosion of artificial intelligence in various fields, providing causal explanations for predictive models becomes urgent. In this study, we investigate causal counterfactual explanation generation and propose CUBE, a causal intervention-based counterfactual explanation method. This work models the counterfactual generation process as a causal intervention model for the first time and employs a causal director to integrate causal relationships. Furthermore, this work proposes a model-based framework to improve counterfactual generation efficiency. The experimental results validate that CUBE outperforms baselines in terms of both lower time costs and higher explanation quality.

Data management system configuration optimization has been the focus of database-related research. It can improve the adaptability of DBMS to various business scenarios by selecting the right parameter combination. However, this has been a daunting task because of the vast number of configuration “knobs” involved, and the information about the effects of each knob usually comes from valuable experience, which is very labor-intensive. While machine learning techniques show promise in certain database tasks, they encounter challenges when applied to configuration optimization. Traditional machine learning models excel in prediction tasks through data fitting, but they lack direct applicability in configuration tuning. Moreover, the lack of explainability in machine learning poses a significant obstacle in quantifying the impact of individual knobs on the database performance. Affected by the above factors, this paper proposes CFTune, a method that can accurately evaluate the performance of a DBMS under each configuration using experience and achieve automatic DBMS configuration tuning with counterfactual techniques. Based on various optimization requirements, the approach can offer configuration tuning advice with minimal modification knobs for DBMS that perform badly under specified workloads. Furthermore, to address model shifts resulting from training data, we offer additional strategies to enhance the robustness of the tuning advice. The experimental results demonstrate that the CFTune effectively optimizes the configuration and enhances the performance.

Recent several years have witnessed the rapid explosion of artificial intelligence applied in various domains with the surpassing human-level performance. Despite the success, these models' underlying mechanisms remain a mystery, as their complicated representations make human understanding impossible. This mystery may cause discrimination and non-robustness in prediction. Making deep learning models more transparent and understandable is gaining popularity, but most of interpretation approaches provide spurious correlations leading to suboptimal, incorrect or even biased interpretations, which could be reduced by causal explanations. Motivated by this, we attempt to study the generation of causal explanations and proposeCUBE, a causal intervention-based counterfactual interpretation method. To ensure that the generation process of counterfactual explanation conforms to causality, we model the counterfactual generation process as a causal graph and construct a counterfactual generation model based on the causal intervention; to generate counterfactuals that adhere to the causality, we introduce a causal director to capture the causal relationships in the distribution and guide the generation of counterfactuals; to improve the efficiency of the counterfactual generation when facing a large number of explanation queries, we model it as a sample generation problem and propose an explainable framework based on adversarial generation. The experimental results validate thatCUBEoutperforms other approaches in terms of both lower time costs and higher explanation quality.

With years of development, a significant number of Time Series Classification (TSC) algorithms have been proposed and applied to various fields such as scientific research and industry scenarios, including traditional statistical methods, machine learning methods, and recently deep learning models. However, choosing a suitable model along with good parameter values that perform well on a given task, which is also known as Combined Algorithm Selection and Hyperparameter optimization problem (CASH), is still challenging. How to automatically select the appropriate algorithm according to the task during analyzing is a topic worthy of further research. Nevertheless, for TSC, a field that has been developed for decades, there is no effective and efficient approach for automatic algorithm selection. To the best of our knowledge, the current approach is based on genetic search, which is very computationally intensive and time-consuming. Therefore, in this paper, we propose TSC-AutoML, a zero-configuration and meta-learning-based approach for the automatic Time Series Classification algorithm CASH (also known as TSC-CASH). TSC-AutoML extracts knowledge from historical tasks and performs automatic feature selection and knowledge filtering with a reinforcement learning policy. The experience extracted is filtered and transformed into metadata. The meta-learner trained on the metadata together with our proposed warm start strategy will select an optimal algorithm for tasks uploaded by users, and then our proposed Hyperparameter Optimization method based on the Fast Warm Start strategy searches for hyperparameter combinations of the selected algorithm and adjusts parameter configuration to achieve top performance. The entire process is pre-trained, automated for the new task, and parameter-free for the user to decide, making it easy for users with the little domain experience to get started easily. Experimental results illustrate that TSC-AutoML outperforms existing methods in terms of both time and accuracy of optimum algorithm selection.