Multi-domain multivariate time series (MTS) forecasting is increasingly important for large-scale and transferable time-series modeling. However, heterogeneous datasets usually contain different numbers of variables, making scalable inter-series dependency modeling challenging. Existing scalable forecasting paradigms often rely on channel-independent (CI) strategies to accommodate variable-dimensional datasets. Nevertheless, by sharing global parameters across independently processed channels, CI models may confuse heterogeneous dependency structures under multi-domain joint training, leading to the dependency confusion problem. In this paper, we argue that effective multi-domain MTS forecasting requires variable-number-agnostic channel-dependence modeling that can explicitly capture fine-grained inter-series dependencies (FID), including both local inter-series dependencies and cross-temporal inter-series dependencies. To this end, we propose the Fine-grained Inter-series Dependency Enhanced (FIDE) framework, a plug-and-play module for patch-based CI forecasters. FIDE introduces dependency prototypes to dynamically perceive input-specific inter-series dependency patterns and employs dependency transmission to propagate dependency information across temporal segments while remaining agnostic to the number of variables. Extensive experiments on eight real-world benchmarks with six representative CI forecasting models demonstrate that FIDE consistently improves forecasting accuracy, effectively mitigates dependency confusion, and achieves statistically significant gains over baseline methods.

With the progressive evolution of large language models (LLMs) and the increasing need of computing for$\mathbf{6 G}$, it becomes crucial for multiple network nodes with limited computing resources to share the need for large model inference. Model partition methods have been proposed to enable computation-intensive artificial intelligence (AI) services by splitting an AI model across multi-edge and cloud nodes. In this paper, a distributed inference optimization framework for transformer decoder-only based LLMs (DIO-LLMs) is proposed in edge-cloud collaborative networks. The partitioning and offloading strategy is determined based on the computing workload and network status. DIO-LLMs specifically accounts for the parallel execution capabilities of the transformer architecture. It employs a two-phase model partitioning strategy, comprising inter-layer and intra-layer partitions, to effectively distribute LLMs across edge and cloud nodes. Additionally, to mitigate inference latency under resource limitations, a Greedy Proximal Policy Optimization (GPPO) based algorithm has been developed to devise optimal strategies. Simulation results indicate that under memory constraints, the proposed algorithm can reduce inference latency more effectively than other baseline algorithms.

Diffusion-based denoising models have demonstrated impressive performance in probabilistic forecasting for multivariate time series (MTS). Nonetheless, existing approaches often model the entire data distribution, neglecting the variability in uncertainty across different components of the time series. This paper introduces a Diffusion-based Decoupled Deterministic and Uncertain ($\mathrm{D^3U}$) framework for probabilistic MTS forecasting. The framework integrates non-probabilistic forecasting with conditional diffusion generation, enabling both accurate point predictions and probabilistic forecasting. $\mathrm{D^3U}$ utilizes a point forecasting model to non-probabilistically model high-certainty components in the time series, generating embedded representations that are conditionally injected into a diffusion model. To better model high-uncertainty components, a patch-based denoising network (PatchDN) is designed in the conditional diffusion model. Designed as a plug-and-play framework, $\mathrm{D^3U}$ can be seamlessly integrated into existing point forecasting models to provide probabilistic forecasting capabilities. It can also be applied to other conditional diffusion methods that incorporate point forecasting models. Experiments on six real-world datasets demonstrate that our method achieves over a 20\% improvement in both point and probabilistic forecasting performance in MTS long-term forecasting compared to state-of-the-art (SOTA) probabilistic forecasting methods. Additionally, extensive ablation studies further validate the effectiveness of the $\mathrm{D^3U}$ framework.

Current time series forecasting models face significant challenges in addressing two key issues inherent in cloud cluster workload forecasting: high missing rates and high dimensionality. To tackle the missing data challenge, we propose a Channel Dependency Pre-learning Module (CD-Block) that combines wavelet decomposition with contrastive learning. This module extracts inter-channel dependencies through self-supervised learning, enhancing the model’s ability to interpret and process incomplete data. For the high dimensionality problem, we introduce a Fourier Graph Network (FGN) that reformulates convolutions in the frequency domain, significantly reducing computational complexity. FGN incorporates a time-frequency alignment loss to align pre-learned channel dependencies with the spectral representations of time series. Building on these innovations, we propose the Pre-learned Dependency Fourier Network (PefNet). Experimental results demonstrate that PefNet achieves superior performance on four high-dimensional benchmark datasets for forecasting tasks and achieves state-of-the-art (SOTA) performance on three real-world cloud cluster workload datasets with different missing data scenarios.

Long-term time series forecasting (LTSF) poses significant challenges due to the need to capture complex long-term dependencies over extended horizons. We revisit the temporal structure of time series data and categorize it into regular periodic pattern and irregular periodic pattern. We argue that a comprehensive modeling of long-term temporal dependencies must simultaneously account for both regular periodic pattern and the latent dependencies present in irregular periodic pattern. However, prior works either focus on regular periodic pattern from a single scale or neglect the rich dependencies within irregular periodic pattern. To address these limitations, we propose PerTime, a multi-scale periodic mixing model for long-term time series forecasting, which consists of two modules: the Periodic Broaden Module (PBM) and the Hierarchical Fusion Module (HFM). PBM enables the extraction of periodic features across multiple time scales, while HFM captures latent long-term dependencies in irregular periodic pattern and integrates information across scales. Extensive experiments on six long-term forecasting benchmark datasets demonstrate that PerTime outperforms existing methods.

Time series forecasting plays a pivotal role in edge intelligence. Current research predominantly focuses on exploring complex model architectures, such as Transformer and Graph Neural Network (GNN), which demonstrate remarkable advantages in capturing high-dimensional complex features. However, these models suffer from inherent limitations in computational efficiency and deployment resource, resulting in significant constraints in temporal efficiency and edge computing compatibility. To address these challenges, this paper proposes a lightweight linear model architecture termed ELinear. By introducing the Linear Channel Fusion module (LCF), the Frequency-domain Multi-Period Awareness mechanism (FMPA) and the Residual Period Fusion module (RPF), we enhance the prediction accuracy of linear models. Experimental results demonstrate that ELinear achieves a 4.7% reduction in Mean Absolute Error (MAE) and a 3.1× improvement in compute speed compared to state-of-the-art (SOTA) models on the widely adopted ETT benchmark dataset.

With the development of virtualization technology, serverless computing has been gaining significant attention in recent years, primarily due to its advantages in scalability and a pay-as-you-go pricing model. In edge networks, the deployment of fine-grained function instances to handle massive request data makes it more difficult to optimize the quality of user experience. This paper addresses the scheduling of heterogeneous function processing requests generated by users in serverless edge computing scenarios, considering the constraints of resources on edge nodes, and making decisions regarding the warm and cold start during the scheduling process. The problem is modeled as a constrained multi-objective optimization issue aimed at minimizing latency and energy consumption. A deep reinforcement learning strategy, grounded in Multi-Agent Proximal Policy Optimization (MAPPO), is introduced to address this challenge, with each user being represented as an autonomous agent. Simulations were conducted to assess the impacts of the learning rate, the request volume, and the size of the input data of the function. The experimental results indicate that, compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), under different scenarios of request scales, the average system delay is reduced by at most 31%, and the average energy consumption is reduced by at most 24%.

As Internet of Things (IoT)-based artificial intelligence (AI) applications grow, the surge in computational and communication demands has raised concerns about energy consumption, making it critical for 6G networks to address this challenge. This paper examines the joint optimization of AI service placement, task scheduling, and computing resource allocation in an edge-network-cloud system to minimize long-term energy consumption. These problems are interdependent: AI service placement determines service locations, influencing task scheduling, which in turn dictates computing resource allocation. The key challenge lies in the coupling of these variables and the two time-scale nature of the problem, involving long-term (AI service placement) and short-term (task scheduling and computing resource allocation) strategies. To address this, a Hierarchical Markov Decision Process (HMDP) framework is proposed for efficient and coordinated optimization across time scales. A Hierarchical Mean-Field Dueling Double Deep Q-Network (HMFD3QN) algorithm is developed within this framework, where the upper layer optimizes AI service placement, and the lower layer manages task scheduling and computing resource allocation. By integrating mean-field theory, the algorithm reduces the complexity of multi-agent interactions. The computing resource allocation problem is shown to be convex when other variables are fixed, and an optimal strategy is derived using Karush-Kuhn-Tucker (KKT) conditions to simplify the action space for reinforcement learning. Experimental results demonstrate that the proposed method can reduce energy consumption by up to 34% compared to baseline methods, significantly improve queue stability, and increase the proportion of tasks meeting QoS requirements.

Network slicing can provide logically isolated networks on the shared network infrastructure by invoking multiple technologies and administrative domains to fulfill end-to-end (E2E) service level agreements (SLAs). To guarantee the E2E service communication quality in the sliced network, an SLA-based cross-domain orchestration framework is proposed in this paper. The framework includes an E2E cross-domain coordination orchestrator at the upper layer and multiple subordinate domain controllers. Furthermore, we design two latency equalization policies applied to the upper layer orchestrator to divide the latency budget for each lower layer domain. Based on the reinforcement learning approach, Double Deep Q-Network with Prioritized Experience Replay (DDQN-PER) and Pointer Network SFC Mapping (PN-SFC), intra-domain resource allocation/mapping algorithms are designed independently for the lower radio access network (RAN) and core network (CN) domain controllers, respectively. The above algorithms are used to jointly optimize the enhanced mobile broadband (eMBB) users service satisfaction level and maximize the number of E2E accessed users. Simulation results show that our proposed algorithm can effectively guarantee the eMBB users QoS and improve the network capacity.