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.

Holographic Storage is an old idea that has always promised high density and fast random access, but has never been commercially competitive with Hard Disk Drives (HDDs) and Solid State Devices (SSDs). In Project HSD at Microsoft Research we asked the question: “Does holographic storage finally make sense for cloud storage?” This article describes our journey toward answering this question. We achieved 1.8× higher density than the previous state-of-the-art, using commodity components available today and leveraging machine learning to compensate for the noise and distortions introduced by commodity components. This uncovered two new challenges which are the focus of this article: achieving high end-to-end energy efficiency without sacrificing capacity, and spatial multiplexing without mechanical movement. Improving end-to-end energy efficiency requires joint optimization across low-level media parameters and higher-level system parameters that govern background maintenance operations such as read refresh and garbage collection. We developed new physics models of the media; analytic and simulation models of the media access and background media maintenance; and workload-driven optimization to find optimal parameter combinations. These techniques resulted in a 14× improvement over the previous approach for typical workloads without sacrificing capacity. We also designed the first scalable and mechanical movement free spatial multiplexing system for holographic storage. Despite these advances, we conclude that currently, holographic storage is still far from the combination of density, capacity scaling, and energy efficiency needed to compete with the incumbent technologies. We need fundamental advances in the physical media that improve energy efficiency by another 1–2 orders of magnitude without reducing data density. Further advances in optics are also required to achieve spatial multiplexing that is simultaneously scalable, low-loss, and high-density.

Time series forecasting plays an important role in numerous real-world domains. Considerable studies have been devoted to prediction by learning temporal features, utilizing improved variants of deep neural networks. However, the variable temporal patterns inherent in complex time series prohibit deep models from discovering reliable dependencies, impairing prediction accuracy. Going beyond previous models, we propose CorrDCN, a decomposed convolutional network with the capability of seasonal autocorrelation 2D-variation modeling. We design the Frequency Guided Decomposition block adaptively configured based on the input series. This facilitates precise series decomposition while allowing CorrDCN to personalize modeling for the decomposed components. Further, we utilize a concise cascaded MLP structure to progressively learn trend variations and integrate local features with global correlations to adequately model seasonal variations. In particular, to tackle the limitations of the 1D structure in simultaneous modeling, we represent seasonal variations in 2D space by reshaping a seasonal 2D tensor based on autocorrelation. This reshaping operation embeds the local features and global correlations of the seasonal series into the rows and columns of the 2D tensor, and thus such seasonal 2D variations can be easily captured by the Multi-scale Inception layers. CorrDCN shows competitive performance on six benchmark datasets. Compared to the mainstream prediction models TimesNet, Non-stationary Transformer and FEDformer, CorrDCN achieves averaged MSE reductions of 4.9%, 17.1% and 19.4%, respectively.