Temporal Query Network for Efficient Multivariate Time Series Forecasting

• URL: http://arxiv.org/abs/2505.12917v1
• Date: Mon, 19 May 2025 09:55:10 GMT
• Title: Temporal Query Network for Efficient Multivariate Time Series Forecasting
• Authors: Shengsheng Lin, Haojun Chen, Haijie Wu, Chunyun Qiu, Weiwei Lin,
• Abstract summary: We propose a novel technique called Temporal Query (TQ) to more effectively capture multivariate correlations.<n>Building upon the TQ technique, we develop a simple yet efficient model named Temporal Query Network (TQNet)<n>Experiments demonstrate that TQNet learns more robust multivariate correlations, achieving state-of-the-art forecasting accuracy across 12 challenging real-world datasets.
• Score: 3.0838061121585616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sufficiently modeling the correlations among variables (aka channels) is crucial for achieving accurate multivariate time series forecasting (MTSF). In this paper, we propose a novel technique called Temporal Query (TQ) to more effectively capture multivariate correlations, thereby improving model performance in MTSF tasks. Technically, the TQ technique employs periodically shifted learnable vectors as queries in the attention mechanism to capture global inter-variable patterns, while the keys and values are derived from the raw input data to encode local, sample-level correlations. Building upon the TQ technique, we develop a simple yet efficient model named Temporal Query Network (TQNet), which employs only a single-layer attention mechanism and a lightweight multi-layer perceptron (MLP). Extensive experiments demonstrate that TQNet learns more robust multivariate correlations, achieving state-of-the-art forecasting accuracy across 12 challenging real-world datasets. Furthermore, TQNet achieves high efficiency comparable to linear-based methods even on high-dimensional datasets, balancing performance and computational cost. The code is available at: https://github.com/ACAT-SCUT/TQNet.

Related papers

• Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering [22.545533166145706]
  We introduce the Forecaster with Offline Clustering Using Segments (FOCUS)<n>FOCUS is a novel approach to MTS forecasting that simplifies long-range dependency modeling.<n>It achieves state-of-the-art accuracy while significantly reducing computational costs.
  arXiv  Detail & Related papers  (2025-05-09T02:34:06Z)
• LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated Autocorrelation for Time Series Forecasting [4.075971633195745]
  We introduce LMS-AutoTSF, a novel time series forecasting architecture that incorporates autocorrelation.<n>Unlike models that rely on predefined trend and seasonal components, LMS-AutoTSF employs two separate encoders per scale.<n>A key innovation in our approach is the integration of autocorrelation, achieved by computing lagged differences in time steps.
  arXiv  Detail & Related papers  (2024-12-09T09:31:58Z)
• UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting [98.12558945781693]
  We propose a transformer-based model UniTST containing a unified attention mechanism on the flattened patch tokens. Although our proposed model employs a simple architecture, it offers compelling performance as shown in our experiments on several datasets for time series forecasting.
  arXiv  Detail & Related papers  (2024-06-07T14:39:28Z)
• MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Model [0.0]
  We introduce the Multi-Channel Data Fusion Network (MCDFN), a hybrid architecture that integrates CNN, Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU)<n>Our comparative benchmarking demonstrates that MCDFN outperforms seven other deep-learning models.<n>This research advances demand forecasting methodologies and offers practical guidelines for integrating MCDFN into supply chain systems.
  arXiv  Detail & Related papers  (2024-05-24T14:30:00Z)
• TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signals [58.865901821451295]
  We present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain, we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel, to efficiently extract rich patterns from the temporal-frequency domain, we introduce a "TC" stream that uses Continuous Wavelet Transform (CWT) to represent information in a 2D tensor form.
  arXiv  Detail & Related papers  (2024-04-15T06:01:48Z)
• Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting [46.63798583414426]
  Long-term time series forecasting (LTSF) represents a critical frontier in time series analysis. Our study demonstrates, through both analytical and empirical evidence, that decomposition is key to containing excessive model inflation. Remarkably, by tailoring decomposition to the intrinsic dynamics of time series data, our proposed model outperforms existing benchmarks.
  arXiv  Detail & Related papers  (2024-01-22T13:15:40Z)
• FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification [53.55504611255664]
  FormerTime is a hierarchical representation model for improving the classification capacity for the multivariate time series classification task. It exhibits three aspects of merits: (1) learning hierarchical multi-scale representations from time series data, (2) inheriting the strength of both transformers and convolutional networks, and (3) tacking the efficiency challenges incurred by the self-attention mechanism.
  arXiv  Detail & Related papers  (2023-02-20T07:46:14Z)
• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm [0.0]
  This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient time-series data (MTSD) We use a recurrent neural network (RNN) based Autoencoder (AE) which is iteratively trained on incoming data. A model of the identified subsequence is saved and used for recognition of repeating subsequences as well as fast offline clustering.
  arXiv  Detail & Related papers  (2022-09-09T09:59:56Z)
• OFedQIT: Communication-Efficient Online Federated Learning via Quantization and Intermittent Transmission [7.6058140480517356]
  Online federated learning (OFL) is a promising framework to collaboratively learn a sequence of non-linear functions (or models) from distributed streaming data. We propose a communication-efficient OFL algorithm (named OFedQIT) by means of a quantization and an intermittent transmission. Our analysis reveals that OFedQIT successfully addresses the drawbacks of OFedAvg while maintaining superior learning accuracy.
  arXiv  Detail & Related papers  (2022-05-13T07:46:43Z)
• Parameter-Efficient Abstractive Question Answering over Tables or Text [60.86457030988444]
  A long-term ambition of information seeking QA systems is to reason over multi-modal contexts and generate natural answers to user queries. Memory intensive pre-trained language models are adapted to downstream tasks such as QA by fine-tuning the model on QA data in a specific modality like unstructured text or structured tables. To avoid training such memory-hungry models while utilizing a uniform architecture for each modality, parameter-efficient adapters add and train small task-specific bottle-neck layers between transformer layers.
  arXiv  Detail & Related papers  (2022-04-07T10:56:29Z)
• Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data [0.0]
  This paper introduces a novel NN architecture which hybridises the Long-Short-Term-Memory (LSTM) and Capsule Networks into a single network. The proposed method uses an unsupervised learning technique to overcome the issues with finding large volumes of labelled training data.
  arXiv  Detail & Related papers  (2022-02-11T10:33:53Z)
• Multi-layer Optimizations for End-to-End Data Analytics [71.05611866288196]
  We introduce Iterative Functional Aggregate Queries (IFAQ), a framework that realizes an alternative approach. IFAQ treats the feature extraction query and the learning task as one program given in the IFAQ's domain-specific language. We show that a Scala implementation of IFAQ can outperform mlpack, Scikit, and specialization by several orders of magnitude for linear regression and regression tree models over several relational datasets.
  arXiv  Detail & Related papers  (2020-01-10T16:14:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.