Multivariate time series (MTS) forecasting is critical in various real-world applications. Recent studies leverage large language models (LLMs) for MTS forecasting, achieving impressive improvements in prediction accuracy and generalization over deep learning-based models. However, these methods often overlook the inherent nonstationarity and domain-specific nature of MTS, as well as the MTS-text modality representation gap, thus limiting the full potential of LLMs in MTS forecasting. In this article, we present A$^{2}$RA-NSMTSllm, a novel multiscale model that adversarially aligns retrieval-augmented LLMs for nonstationary MTS forecasting (A$^{2}$RA-NSMTSllm). Specifically, A$^{2}$RA-NSMTSllm employs a frequency-guided multiscale decomposition normalization–denormalization Framework, which adaptively removes and recovers the nonstationarity based on multiscale distribution dynamics modeling. To bridge the gap between MTS and LLM semantic space, it develops a time series–text alignment reconstruction-enhanced generative adversarial network, where the generator effectively learns to produce LLM-friendly TS embeddings without losing key temporal features. Furthermore, A$^{2}$RA-NSMTSllm introduces a TS knowledge base retrieval-augmented mechanism to enhance domain-specific forecasting, dynamically retrieving and integrating relevant knowledge to guide LLM temporal reasoning and forecasting. Extensive experiments show that A$^{2}$RA-NSMTSllm achieves superior forecasting performance and generalization, outperforming the latest CALF by 8.19%/5.27%, 11.01%/8.66%, and 20.94%/10.84% in MSE/MAE across full-shot, few-shot, and zero-shot scenarios.

Mapping and localization is a critical module of autonomous driving, and significant achievements have been reached in this field. Beyond Global Navigation Satellite System (GNSS), research in point cloud registration, visual feature matching, and inertia navigation has greatly enhanced the accuracy and robustness of mapping and localization in different scenarios. However, highly urbanized scenes are still challenging: LIDAR- and camera-based methods perform poorly with numerous dynamic objects; the GNSS-based solutions experience signal loss and multi-path problems; the inertia measurement units (IMU) suffer from drifting. Unfortunately, current public datasets either do not adequately address this urban challenge or do not provide enough sensor information related to map-ping and localization. Here we present UrbanLoco: a mapping/localization dataset collected in highly-urbanized environments with a full sensor-suite. The dataset includes 13 trajectories collected in San Francisco and Hong Kong, covering a total length of over 40 kilometers. Our dataset includes a wide variety of urban terrains: urban canyons, bridges, tunnels, sharp turns, etc. More importantly, our dataset includes information from LIDAR, cameras, IMU, and GNSS receivers. Now the dataset is publicly available through the link in the footnote1.

Focus on the first China domestic coking flue gas desulfurization and denitriation integrated device, in order to solve the problem that the entrance parameters fluctuate and a detection lag exists due to the upstream coking workshop, which is extremely unfavorable to the optimal control of desulfurization and denitriation process. An intelligent integrated prediction model of flue gas SO2 concentration, O2 content and NOx concentration was proposed: the mechanism models of SO2, NOx concentration and O2 content were established according to the principle of material balance and reaction kinetics, respectively. For the prediction error, raw data was pretreated and the auxiliary variables were determined by principal component analysis, in order to improve the training speed and generalization ability of neural network, an improved RBFNN combining optimal stopping principle and dual momentum adaptive learning rate was proposed and used to compensate the error. Based on the practical data of two 55-hole and 6-meter top charging coke ovens in the coking group, the effectiveness and superiority of proposed model and method were verified by simulation via comparison of various models.