Models based on neural networks and machine learning are seeing a rise in popularity in space physics. In particular, the forecasting of geomagnetic indices with neural network models is becoming a popular field of study. These models are evaluated with metrics such as the root-mean-square error (RMSE) and Pearson correlation coefficient. However, these classical metrics sometimes fail to capture crucial behavior. To show where the classical metrics are lacking, we trained a neural network, using a long short-term memory network, to make a forecast of the disturbance storm time index at origin time $t$ with a forecasting horizon of 1 up to 6 hours, trained on OMNIWeb data. Inspection of the model's results with the correlation coefficient and RMSE indicated a performance comparable to the latest publications. However, visual inspection showed that the predictions made by the neural network were behaving similarly to the persistence model. In this work, a new method is proposed to measure whether two time series are shifted in time with respect to each other, such as the persistence model output versus the observation. The new measure, based on Dynamical Time Warping, is capable of identifying results made by the persistence model and shows promising results in confirming the visual observations of the neural network's output. Finally, different methodologies for training the neural network are explored in order to remove the persistence behavior from the results.
TLCCSP: A Scalable Framework for Enhancing Time Series Forecasting with Time-Lagged Cross-Correlations
School of Artificial Intelligence（人工智能学院）；Institute of Artificial Intelligence and Future Networks（人工智能与未来网络研究所）；Department of Systems Science, Faculty of Arts and Sciences（艺术与科学学院系统科学系）；Guangdong Key Lab of AI and Multi-Modal Data Processing（广东人工智能与多模态数据处理重点实验室）；BNU-HKBU United International College（北师大-香港大学联合国际学院）
L-GTA: Latent Generative Modeling for Time Series Augmentation
Faculdade de Engenharia da Universidade do Porto（葡萄牙波尔图大学工程学院）；Fraunhofer Portugal AICOS（德国弗劳恩霍夫葡萄牙AICOS研究所）；University of Coimbra（科英布拉大学）；Dalhousie University（达尔豪斯大学）
Diffusion-Based Scenario Tree Generation for Multivariate Time Series Prediction and Multistage Stochastic Optimization
HERON - Hellenic Robotics Center of Excellence（希腊机器人 excellence 中心）；Robotics Institute（机器人研究所）；School of Electrical & Computer Engineering（电气与计算机工程学院）；National Technical University of Athens（雅典技术大学）
Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Huazhong University of Science and Technology（华中科技大学）；The University of New South Wales（新南威尔士大学）；Aalborg University（奥尔堡大学）
Learning non-Gaussian Time Series using the Box-Cox Gaussian Process
Center for Mathematical Modeling and Department of Mathematical Engineering Universidad de Chile（智利大学数学建模中心和数学工程系）；Center for Mathematical Modeling Universidad de Chile（智利大学数学建模中心）
Forecasting Local Ionospheric Parameters Using Transformers
Atmospheric Propagation Branch, Naval Information Warfare Center Pacific（海军信息战中心太平洋分部大气传播分支）；Department of Mathematics and Statistics, San Diego State University（圣地亚哥州立大学数学与统计学系）；Department of Computer Science, UC Irvine（UC伊沃特大学计算机科学系）；Space Science Division, Naval Research Laboratory（海军研究实验室空间科学分部）
Federated Dynamic Modeling and Learning for Spatiotemporal Data Forecasting
COSYS-GRETTIA, Gustave Eiffel University, 77420 France（COSYS-GRETTIA，巴黎-伊夫林大学）；ENTPE, University of Lyon（ENTPE，里昂大学）；the LICIT-ECO7 University Gustave Eiffel, France（LICIT-ECO7 巴黎-伊夫林大学）
