Spatio-temporal forecasting is crucial in real-world dynamic systems, predicting future changes using historical data from diverse locations. Existing methods often prioritize the development of intricate neural networks to capture the complex dependencies of the data, yet their accuracy fails to show sustained improvement. Besides, these methods also overlook node heterogeneity, hindering customized prediction modules from handling diverse regional nodes effectively. In this paper, our goal is not to propose a new model but to present a novel low-rank adaptation framework as an off-the-shelf plugin for existing spatial-temporal prediction models, termed ST-LoRA, which alleviates the aforementioned problems through node-level adjustments. Specifically, we first tailor a node adaptive low-rank layer comprising multiple trainable low-rank matrices. Additionally, we devise a multi-layer residual fusion stacking module, injecting the low-rank adapters into predictor modules of various models. Across six real-world traffic datasets and six different types of spatio-temporal prediction models, our approach minimally increases the parameters and training time of the original models by less than 4%, still achieving consistent and sustained performance enhancement.

Brown-Forman, whose brands include Old Forester and Woodford Reserve bourbon, has spent the past three years taking inventory and integrating diverse pools of consumer, production and sales data across its global operations, as part of a broader effort to update an aging technology stack, Mr. Nall said. That was no small task. Founded nearly 150 year ago, Brown-Forman today has some 4,800 employees and operates in more than 170 countries world-wide. Since becoming CIO in 2015, Mr. Nall has led a gradual strategic shift in the role of the company's enterprise information-technology hub, from a backroom tech support service to a business partner aligned with marketing and sales teams, as well as other corporate and global production functions. That shift has seen data scientists and other IT pros increasingly working across the entire business on efforts to drive efficiencies and generate revenue: "Technology is interwoven into the whole process," he said. Nowhere is the need for a more business-oriented IT model more clear than with the emerging powers of artificial intelligence and machine learning to supercharge corporate decision-making, he said.