New micro-grid design with renewable energy sources and battery storage systems can help improve greenhouse gas emissions and reduce the operational cost. To provide an effective short-/long-term forecasting of both energy generation and load demand, time series predictive modeling has been one of the key tools to guide the optimal decision-making for planning and operation. One of the critical challenges of time series renewable energy forecasting is the lack of historical data to train an adequate predictive model. Moreover, the performance of a machine learning model is sensitive to the choice of its corresponding hyperparameters. Bearing these considerations in mind, this paper develops a BiLO-Auto-TSF/ML framework that automates the optimal design of a few-shot learning pipeline from a bi-level programming perspective. Specifically, the lower-level meta-learning helps boost the base-learner to mitigate the small data challenge while the hyperparameter optimization at the upper level proactively searches for the optimal hyperparameter configurations for both base- and meta-learners. Note that the proposed framework is so general that any off-the-shelf machine learning method can be used in a plug-in manner. Comprehensive experiments fully demonstrate the effectiveness of our proposed BiLO-Auto-TSF/ML framework to search for a high-performance few-shot learning pipeline for various energy sources.
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；RWTH Aachen University（亚琛工业大学）；Forschungszentrum Jülich GmbH（于利希研究中心有限公司）
Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes the five sections (1) data pre-processing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever-growing demand for time series forecasts is automating this design process. The p…
Leibniz University Hannover（汉诺威莱布尼茨大学）；Ludwig-Maximilian University（路德维希-马克西米利安大学）；Fraunhofer Institute for Integrated Circuits (IIS)（弗劳恩霍夫集成电路研究所）；University of Freiburg（弗莱堡大学）；Bosch Center for Artificial Intelligence（博世人工智能中心）
Recent years have witnessed tremendously improved efficiency of Automated Machine Learning (AutoML), especially Automated Deep Learning (AutoDL) systems, but recent work focuses on tabular, image, or NLP tasks. So far, little attention has been paid to general AutoDL frameworks for time series forecasting, despite the enormous success in applying different novel architectures to such tasks. In this paper, we propose an efficient approach for the joint optimization of neural architecture and hyperparameters of the entire data processing pipeline for time series forecasting. In contrast to commo…
Aalborg University（奥尔堡大学）；University of Southern California（南加州大学）
Correlated time series (CTS) forecasting plays an essential role in many cyber-physical systems, where multiple sensors emit time series that capture interconnected processes. Solutions based on deep learning that deliver state-of-the-art CTS forecasting performance employ a variety of spatio-temporal (ST) blocks that are able to model temporal dependencies and spatial correlations among time series. However, two challenges remain. First, ST-blocks are designed manually, which is time consuming and costly. Second, existing forecasting models simply stack the same ST-blocks multiple times, whic…
This paper considers improved forecasting in possibly nonlinear dynamic settings, with high-dimension predictors ("big data" environments). To overcome the curse of dimensionality and manage data and model complexity, we examine shrinkage estimation of a back-propagation algorithm of a deep neural net with skip-layer connections. We expressly include both linear and nonlinear components. This is a high-dimensional learning approach including both sparsity L1 and smoothness L2 penalties, allowing high-dimensionality and nonlinearity to be accommodated in one step. This approach selects signific…
Sensors in cyber-physical systems often capture interconnected processes and thus emit correlated time series (CTS), the forecasting of which enables important applications. The key to successful CTS forecasting is to uncover the temporal dynamics of time series and the spatial correlations among time series. Deep learning-based solutions exhibit impressive performance at discerning these aspects. In particular, automated CTS forecasting, where the design of an optimal deep learning architecture is automated, enables forecasting accuracy that surpasses what has been achieved by manual approach…
Norwegian University of Science and Technology（挪威科技大学）
This paper presents an ensemble forecasting method that shows strong results on the M4 Competition dataset by decreasing feature and model selection assumptions, termed DONUT (DO Not UTilize human beliefs). Our assumption reductions, primarily consisting of auto-generated features and a more diverse model pool for the ensemble, significantly outperform the statistical, feature-based ensemble method FFORMA by Montero-Manso et al. (2020). We also investigate feature extraction with a Long Short-term Memory Network (LSTM) Autoencoder and find that such features contain crucial information not cap…
TSGym: Design Choices for Deep Multivariate Time-Series Forecasting
AI Lab, Shanghai University of Finance and Economics（上海财经大学金融学院人工智能实验室）；Ant Group（蚂蚁集团）；MoE Key Laboratory of Interdisciplinary Research of Computation and Economics（计算与经济学交叉学科关键实验室）
Choosing the technique that is the best at forecasting your data, is a problem that arises in any forecasting application. Decades of research have resulted into an enormous amount of forecasting methods that stem from statistics, econometrics and machine learning (ML), which leads to a very difficult and elaborate choice to make in any forecasting exercise. This paper aims to facilitate this process for high-level tactical sales forecasts by comparing a large array of techniques for 35 times series that consist of both industry data from the Coca-Cola Company and publicly available datasets. …
Beihang University（北京航空航天大学）；Central University of Finance and Economics（中央财经大学）
Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting community. Nonetheless, most of the existing approaches depend on the manual choice of an appropriate set of features. Exploiting machine learning methods to extract features from time series automatically becomes crucial in state-of-the-art time series analysis. In this paper, we introduce an automated approach to extract time series features …
Kwasan and Hida Observatories, Kyoto University（京都大学花山天文台与飞驒天文台）；RIKEN Advanced Institute for Computational Science（理化学研究所计算科学研究院）；Unit of Synergetic Studies for Space, Kyoto University（京都大学空间协同研究单元）；BroadBand Tower, Inc.（BroadBand Tower 公司）
Automated forecasts serve important role in space weather science, by providing statistical insights to flare-trigger mechanisms, and by enabling tailor-made forecasts and high-frequency forecasts. Only by realtime forecast we can experimentally measure the performance of flare-forecasting methods while confidently avoiding overlearning. We have been operating unmanned flare forecast service since August, 2015 that provides 24-hour-ahead forecast of solar flares, every 12 minutes. We report the method and prediction results of the system.
University of Patras（帕特雷大学）；Roma Tre University（罗马第三大学）；National Technical University of Athens（雅典国立技术大学）
Machine and statistical learning algorithms can be reliably automated and applied at scale. Therefore, they can constitute a considerable asset for designing practical forecasting systems, such as those related to urban water demand. Quantile regression algorithms are statistical and machine learning algorithms that can provide probabilistic forecasts in a straightforward way, and have not been applied so far for urban water demand forecasting. In this work, we aim to fill this gap by automating and extensively comparing several quantile-regression-based practical systems for probabilistic one…
Northeast Electric Power University（东北电力大学）；State Grid Beijing Electric Power Company（国网北京市电力公司）
In order to reduce the negative impact of the uncertainty of load and renewable energies outputs on microgrid operation, an optimal scheduling model is proposed for isolated microgrids by using automated reinforcement learning-based multi-period forecasting of renewable power generations and loads. Firstly, a prioritized experience replay automated reinforcement learning (PER-AutoRL) is designed to simplify the deployment of deep reinforcement learning (DRL)-based forecasting model in a customized manner, the single-step multi-period forecasting method based on PER-AutoRL is proposed for the f…
We present a new algorithm for approximate inference in probabilistic programs, based on a stochastic gradient for variational programs. This method is efficient without restrictions on the probabilistic program; it is particularly practical for distributions which are not analytically tractable, including highly structured distributions that arise in probabilistic programs. We show how to automatically derive mean-field probabilistic programs and optimize them, and demonstrate that our perspective improves inference efficiency over other algorithms.
We develop a general method for incentive-based programming of hybrid quantum-classical computing systems using reinforcement learning, and apply this to solve combinatorial optimization problems on both simulated and real gate-based quantum computers. Relative to a set of randomly generated problem instances, agents trained through reinforcement learning techniques are capable of producing short quantum programs which generate high quality solutions on both types of quantum resources. We observe generalization to problems outside of the training set, as well as generalization from the simulat…
This work offers a broad perspective on probabilistic modeling and inference in light of recent advances in probabilistic programming, in which models are formally expressed in Turing-complete programming languages. We consider a typical workflow and how probabilistic programming languages can help to automate this workflow, especially in the matching of models with inference methods. We focus on two properties of a model that are critical in this matching: its structure---the conditional dependencies between random variables---and its form---the precise mathematical definition of those depend…
Most of the existing information retrieval systems are based on bag of words model and are not equipped with common world knowledge. Work has been done towards improving the efficiency of such systems by using intelligent algorithms to generate search queries, however, not much research has been done in the direction of incorporating human-and-society level knowledge in the queries. This paper is one of the first attempts where such information is incorporated into the search queries using Wikipedia semantics. The paper presents an essential shift from conventional token based queries to conce…
CICESE Research Center（CICESE研究中心）；Universidad Anahuac Querétaro（阿纳瓦克克雷塔罗大学）；Universidad Nacional Autónoma de México（墨西哥国立自治大学）；Universidad Autónoma de San Luis Potosí（圣路易斯波托西自治大学）；Universidad Autónoma de Tlaxcala（特拉斯卡拉自治大学）
Despite recent improvements in computer vision, artificial visual systems' design is still daunting since an explanation of visual computing algorithms remains elusive. Salient object detection is one problem that is still open due to the difficulty of understanding the brain's inner workings. Progress on this research area follows the traditional path of hand-made designs using neuroscience knowledge. In recent years two different approaches based on genetic programming appear to enhance their technique. One follows the idea of combining previous hand-made methods through genetic programming …
ACE-RLHF: Automated Code Evaluation and Socratic Feedback Generation Tool using Large Language Models and Reinforcement Learning with Human Feedback
Department of Computer Science, Cleveland State University（克利夫兰州立大学计算机科学系）；School of Computing and Information Sciences, Florida International University（佛罗里达国际大学计算与信息科学学院）；Data Science and Learning Division, Argonne National Laboratory（阿贡国家实验室数据科学与学习分部）
Neural networks are sensitive to hyper-parameter and architecture choices. Automated Machine Learning (AutoML) is a promising paradigm for automating these choices. Current ML software libraries, however, are quite limited in handling the dynamic interactions among the components of AutoML. For example, efficientNAS algorithms, such as ENAS and DARTS, typically require an implementation coupling between the search space and search algorithm, the two key components in AutoML. Furthermore, implementing a complex search flow, such as searching architectures within a loop of searching hardware con…
Machine learning has been gaining traction in recent years to meet the demand for tools that can efficiently analyze and make sense of the ever-growing databases of biomedical data in health care systems around the world. However, effectively using machine learning methods requires considerable domain expertise, which can be a barrier of entry for bioinformaticians new to computational data science methods. Therefore, off-the-shelf tools that make machine learning more accessible can prove invaluable for bioinformaticians. To this end, we have developed an open source pipeline optimization too…
Netaji Subhas Institute of Technology（内塔吉·苏巴斯理工学院）；Microsoft Research（微软研究院）
We present a method for automatically generating repair feedback for syntax errors for introductory programming problems. Syntax errors constitute one of the largest classes of errors (34%) in our dataset of student submissions obtained from a MOOC course on edX. The previous techniques for generating automated feed- back on programming assignments have focused on functional correctness and style considerations of student programs. These techniques analyze the program AST of the program and then perform some dynamic and symbolic analyses to compute repair feedback. Unfortunately, it is not pos…
Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review
Data Science and Computational Intelligence Institute, University of Granada（格拉纳达大学数据科学与计算智能研究所）；Ferdowsi University of Mashhad（马什哈德费尔多西大学）；Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University（迪肯大学智能系统研究与创新研究所）；K. N. Toosi University of Technology（哈迪·纳赛尔·托西理工大学）；Dibrugarh University（迪布鲁加尔大学）；Islamic Azad University（伊斯兰阿扎德大学）；Rajaie Cardiovascular Medical and Research Center（拉杰伊心血管医学与研究中心）；Iran University of Medical Sciences（伊朗医科大学）；University of Southern Queensland（南昆士兰大学）；University of Cambridge（剑桥大学）
Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests
University of Bucharest Faculty of Mathematics and Computer Science（布加勒斯大学数学与计算机科学学院）；Softbinator Technologies（Softbinator技术公司）；Delft University of Technology（代尔夫特理工大学）
本文提出利用生成式 AI 自动生成编程竞赛高质量测试用例的 NLP 方法，并在 OJI、Kilonova.ro 和 IIOT 数据集上验证其可发现隐藏错误、减轻教师负担。
This study introduces a framework for the forecasting, reconstruction and feature engineering of multivariate processes along with its renewable energy applications. We integrate derivative-free optimization with an ensemble of sequence-to-sequence networks and design a new resampling technique called additive resampling, which, along with Bootstrap aggregating (bagging) resampling, are applied to initialize the ensemble structure. Moreover, we explore the proposed framework performance on three renewable energy sources---wind, solar and ocean wave---and conduct several short- to long-term for…
