TSRM: A Lightweight Temporal Feature Encoding Architecture for Time Series Forecasting and Imputation

• URL: http://arxiv.org/abs/2504.18878v1
• Date: Sat, 26 Apr 2025 09:53:20 GMT
• Title: TSRM: A Lightweight Temporal Feature Encoding Architecture for Time Series Forecasting and Imputation
• Authors: Robert Leppich, Michael Stenger, Daniel Grillmeyer, Vanessa Borst, Samuel Kounev,
• Abstract summary: We introduce a temporal feature encoding architecture called Time Series Representation Model (TSRM) for time series forecasting and imputation.<n>The architecture is structured around CNN-based representation layers, each dedicated to an independent representation learning task.<n>The architecture is fundamentally based on a configuration that is inspired by a Transformer encoder, with self-attention mechanisms at its core.
• Score: 1.7819099868722776
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a temporal feature encoding architecture called Time Series Representation Model (TSRM) for multivariate time series forecasting and imputation. The architecture is structured around CNN-based representation layers, each dedicated to an independent representation learning task and designed to capture diverse temporal patterns, followed by an attention-based feature extraction layer and a merge layer, designed to aggregate extracted features. The architecture is fundamentally based on a configuration that is inspired by a Transformer encoder, with self-attention mechanisms at its core. The TSRM architecture outperforms state-of-the-art approaches on most of the seven established benchmark datasets considered in our empirical evaluation for both forecasting and imputation tasks. At the same time, it significantly reduces complexity in the form of learnable parameters. The source code is available at https://github.com/RobertLeppich/TSRM.

Related papers

• PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting [82.03373838627606]
  Self-attention mechanism in Transformer architecture requires positional embeddings to encode temporal order in time series prediction. We argue that this reliance on positional embeddings restricts the Transformer's ability to effectively represent temporal sequences. We present a model integrating PRE with a standard Transformer encoder, demonstrating state-of-the-art performance on various real-world datasets.
  arXiv  Detail & Related papers  (2024-08-20T01:56:07Z)
• Time Series Representation Models [2.724184832774005]
  Time series analysis remains a major challenge due to its sparse characteristics, high dimensionality, and inconsistent data quality. Recent advancements in transformer-based techniques have enhanced capabilities in forecasting and imputation. We propose a new architectural concept for time series analysis based on introspection.
  arXiv  Detail & Related papers  (2024-05-28T13:25:31Z)
• 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)
• HyperTime: Implicit Neural Representation for Time Series [131.57172578210256]
  Implicit neural representations (INRs) have recently emerged as a powerful tool that provides an accurate and resolution-independent encoding of data. In this paper, we analyze the representation of time series using INRs, comparing different activation functions in terms of reconstruction accuracy and training convergence speed. We propose a hypernetwork architecture that leverages INRs to learn a compressed latent representation of an entire time series dataset.
  arXiv  Detail & Related papers  (2022-08-11T14:05:51Z)
• TMS: A Temporal Multi-scale Backbone Design for Speaker Embedding [60.292702363839716]
  Current SOTA backbone networks for speaker embedding are designed to aggregate multi-scale features from an utterance with multi-branch network architectures for speaker representation. We propose an effective temporal multi-scale (TMS) model where multi-scale branches could be efficiently designed in a speaker embedding network almost without increasing computational costs.
  arXiv  Detail & Related papers  (2022-03-17T05:49:35Z)
• Merlion: A Machine Learning Library for Time Series [73.46386700728577]
  Merlion is an open-source machine learning library for time series. It features a unified interface for models and datasets for anomaly detection and forecasting. Merlion also provides a unique evaluation framework that simulates the live deployment and re-training of a model in production.
  arXiv  Detail & Related papers  (2021-09-20T02:03:43Z)
• TAM: Temporal Adaptive Module for Video Recognition [60.83208364110288]
  temporal adaptive module (bf TAM) generates video-specific temporal kernels based on its own feature map. Experiments on Kinetics-400 and Something-Something datasets demonstrate that our TAM outperforms other temporal modeling methods consistently.
  arXiv  Detail & Related papers  (2020-05-14T08:22:45Z)
• ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series Forecasting [6.043572971237165]
  We propose ForecastNet, which uses a deep feed-forward architecture to provide a time-variant model. ForecastNet is demonstrated to outperform statistical and deep learning benchmark models on several datasets.
  arXiv  Detail & Related papers  (2020-02-11T01:03:33Z)
• Stacked Boosters Network Architecture for Short Term Load Forecasting in Buildings [0.0]
  This paper presents a novel deep learning architecture for short term load forecasting of building energy loads. The architecture is based on a simple base learner and multiple boosting systems that are modelled as a single deep neural network. The architecture is evaluated in several short-term load forecasting tasks with energy data from an office building in Finland.
  arXiv  Detail & Related papers  (2020-01-23T08:35:36Z)
• A Deep Structural Model for Analyzing Correlated Multivariate Time Series [11.009809732645888]
  We present a deep learning structural time series model which can handle correlated multivariate time series input. The model explicitly learns/extracts the trend, seasonality, and event components. We compare our model with several state-of-the-art methods through a comprehensive set of experiments on a variety of time series data sets.
  arXiv  Detail & Related papers  (2020-01-02T18:48:29Z)

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.