Pre-trained models have demonstrated exceptional generalization capabilities in time-series forecasting; however, adapting them to evolving data distributions remains a significant challenge. A key hurdle lies in accessing the original training data, as fine-tuning solely on new data often leads to catastrophic forgetting. To address this issue, we propose Replay Tuning (R-Tuning), a novel framework designed for the continual adaptation of pre-trained time-series models. R-Tuning constructs a unified latent space that captures both prior and current task knowledge through a frequency-aware replay strategy. Specifically, it augments model-generated samples via wavelet-based decomposition across multiple frequency bands, generating trend-preserving and fusion-enhanced variants to improve representation diversity and replay efficiency. To further reduce reliance on synthetic samples, R-Tuning introduces a latent consistency constraint that aligns new representations with the prior task space. This constraint guides joint optimization within a compact and semantically coherent latent space, ensuring robust knowledge retention and adaptation. Extensive experimental results demonstrate the superiority of R-Tuning, which reduces MAE and MSE by up to 46.9% and 46.8%, respectively, on new tasks, while preserving prior knowledge with gains of up to 5.7% and 6.0% on old tasks. Notably, under few-shot settings, R-Tuning outperforms all state-of-the-art baselines even when synthetic proxy samples account for only 5% of the new task dataset.

Encoding time series into tokens and using language models for processing has been shown to substantially augment the models' ability to generalize to unseen tasks. However, existing language models for time series forecasting encounter several obstacles, including aliasing distortion and prolonged inference times, primarily due to the limitations of quantization processes and the computational demands of large models. This paper introduces Apollo-Forecast, a novel framework that tackles these challenges with two key innovations: the Anti-Aliasing Quantization Module (AAQM) and the Race Decoding (RD) technique. AAQM adeptly encodes sequences into tokens while mitigating high-frequency noise in the original signals, thus enhancing both signal fidelity and overall quantization efficiency. RD employs a draft model to enable parallel processing and results integration, which markedly accelerates the inference speed for long-term predictions, particularly in large-scale models. Extensive experiments on various real-world datasets show that Apollo-Forecast outperforms state-of-the-art methods by 35.41% and 18.99% in WQL and MASE metrics, respectively, in zero-shot scenarios. Furthermore, our method achieves an acceleration of 1.9X-2.7X in inference speed over the baseline methods.

Industrial time series prediction (ITSP) is critical to the predictive maintenance system of modern industry. However, time-varying conditions and complex industrial processes cause the distribution drift of industrial time series, raising the difficulty of prediction. This article proposes an ITSP model considering distribution information, namely MDEformer. First, the multivariate dynamic embedding (MDE) is designed to provide the property of the channel-binding dynamic distribution awareness. Specifically, a dynamic mode transition and selection module is adopted to exploit dynamic distribution features of time series, and the bidirectional dynamic residual connection integrates dynamic distribution information into embedding vectors to filter distribution change interference. Then, the vanilla Transformer encoder is used to achieve multivariate prediction. Finally, a generative pretraining and fine-tuning strategy is used to enhance the generalization ability in real production scenarios. Extensive results on a real-world zinc smelting dataset illustrate the superiority of MDEformer.

Industrial time series prediction (ITSP) is an indispensable part of predictive control in modern industry. Recently, supervised deep learning-based methods have provided solutions with sufficient annotated data. However, there is massive unlabeled data with complex temporal features in modern industrial production, resulting in poor performance of these methods. To address this problem, a self-supervised generative pre-trained model with a learnable mask network (SSGPM-LMN) is proposed in this paper. First, the multivariate time series are made into patches channel-independently. Then, these patches are fed into a Transformer encoder with the learnable mask-reconstruction paradigm, drawing mask indices with high temporal features by calculating the cosine similarity in low-dimensional feature space to better learn general representations. Furthermore, a two-step fine-tuning strategy, including linear probing and full fine-tuning, is adopted for various downstream scenarios. Finally, extensive experimental results on case studies of ITSP and transfer learning indicate that our SSGPM-LMN achieves superior performance.

At present, data-driven fault diagnosis methods have made excellent achievements. In industrial scenarios, it is difficult to obtain sufficient amount of fault data, which means intelligent fault diagnosis is often faced with imbalanced data problem. Moreover, the label noise is usually brought due to manual recording errors so as to seriously affect the diagnosis performance. To address these problems, this paper proposed a safe-domain generative adversarial network with Transformer (SDGAN). A safe domain selecting method is used to remove the noisy samples and construct a pure dataset which poses no risk to the training process of GAN. Therefore, GAN is able to generate high-quality minority samples to balance the original dataset. In addition, the Vision Transformer (ViT) is also applied as a classifier to recognize the global information for each fault sample and achieve high diagnostic accuracy. The experimental results show that SDGAN achieves great diagnosis performance on various imbalanced ratios and noise ratios cases. Furthermore, SDGAN outperforms other baseline methods on imbalanced fault diagnosis with label noise, which indicates that the SDGAN can effectively solve real-world industrial problems.

Mechanical fault diagnosis is crucial to ensure the safe operations of equipment in intelligent manufacturing systems. Deep learning-based methods have been recently developed for fault diagnosis due to their advantages in feature representation. However, most of these methods fail to learn relations between samples and thus perform poorly without sufficient labeled data. In this article, we propose a new few-shot learning method named dual graph neural network (DGNNet) with residual blocks to address fault diagnosis problems with limited data. First, the residual module learns the feature of samples with image data transferred from original signals. Second, two complete graphs built on the sample features are used to extract the instance-level and distribution-level relations between samples. In particular, an alternate update policy between the instance and distribution graphs integrates the multilevel relations to propagate the label information of a few labeled samples to unlabeled samples. This technique leverages labeled and unlabeled samples to identify unseen faults, encouraging DGNNet competency in fault diagnosis tasks with very few labeled samples. Extensive results on various datasets show that DGNNet achieves excellent performance in supervised fault diagnosis tasks and outperforms baselines by a great margin in semisupervised cases.