LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated Autocorrelation for Time Series Forecasting

• URL: http://arxiv.org/abs/2412.06866v3
• Date: Tue, 07 Jan 2025 16:16:49 GMT
• Title: LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated Autocorrelation for Time Series Forecasting
• Authors: Ibrahim Delibasoglu, Sanjay Chakraborty, Fredrik Heintz,
• Abstract summary: 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.
• Score: 4.075971633195745
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Time series forecasting is an important challenge with significant applications in areas such as weather prediction, stock market analysis, scientific simulations and industrial process analysis. In this work, we introduce LMS-AutoTSF, a novel time series forecasting architecture that incorporates autocorrelation while leveraging dual encoders operating at multiple scales. Unlike models that rely on predefined trend and seasonal components, LMS-AutoTSF employs two separate encoders per scale: one focusing on low-pass filtering to capture trends and the other utilizing high-pass filtering to model seasonal variations. These filters are learnable, allowing the model to dynamically adapt and isolate trend and seasonal components directly in the frequency domain. A key innovation in our approach is the integration of autocorrelation, achieved by computing lagged differences in time steps, which enables the model to capture dependencies across time more effectively. Each encoder processes the input through fully connected layers to handle temporal and channel interactions. By combining frequency-domain filtering, autocorrelation-based temporal modeling, and channel-wise transformations, LMS-AutoTSF not only accurately captures long-term dependencies and fine-grained patterns but also operates more efficiently compared to other state-of-the-art methods. Its lightweight design ensures faster processing while maintaining high precision in forecasting across diverse time horizons. The source code is publicly available at \url{http://github.com/mribrahim/LMS-TSF}

Related papers

• MFRS: A Multi-Frequency Reference Series Approach to Scalable and Accurate Time-Series Forecasting [51.94256702463408]
  Time series predictability is derived from periodic characteristics at different frequencies. We propose a novel time series forecasting method based on multi-frequency reference series correlation analysis. Experiments on major open and synthetic datasets show state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-03-11T11:40:14Z)
• Oscillatory State-Space Models [61.923849241099184]
  We propose Lineary State-Space models (LinOSS) for efficiently learning on long sequences. A stable discretization, integrated over time using fast associative parallel scans, yields the proposed state-space model. We show that LinOSS is universal, i.e., it can approximate any continuous and causal operator mapping between time-varying functions.
  arXiv  Detail & Related papers  (2024-10-04T22:00:13Z)
• MixLinear: Extreme Low Resource Multivariate Time Series Forecasting with 0.1K Parameters [6.733646592789575]
  Long-term Time Series Forecasting (LTSF) involves predicting long-term values by analyzing a large amount of historical time-series data to identify patterns and trends. Transformer-based models offer high forecasting accuracy, but they are often too compute-intensive to be deployed on devices with hardware constraints. We propose MixLinear, an ultra-lightweight time series forecasting model specifically designed for resource-constrained devices.
  arXiv  Detail & Related papers  (2024-10-02T23:04:57Z)
• FAITH: Frequency-domain Attention In Two Horizons for Time Series Forecasting [13.253624747448935]
  Time Series Forecasting plays a crucial role in various fields such as industrial equipment maintenance, meteorology, energy consumption, traffic flow and financial investment. Current deep learning-based predictive models often exhibit a significant deviation between their forecasting outcomes and the ground truth. We propose a novel model Frequency-domain Attention In Two Horizons, which decomposes time series into trend and seasonal components.
  arXiv  Detail & Related papers  (2024-05-22T02:37:02Z)
• 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)
• Combining Slow and Fast: Complementary Filtering for Dynamics Learning [9.11991227308599]
  We propose a learning-based model learning approach to dynamics model learning. We also propose a hybrid model that requires an additional physics-based simulator.
  arXiv  Detail & Related papers  (2023-02-27T13:32:47Z)
• 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)
• Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System [46.39662315849883]
  We introduce CLMFormer, a novel framework that mitigates redundancy through curriculum learning and a memory-driven decoder. CLMFormer consistently improves Transformer-based models by up to 30%, demonstrating its effectiveness in long-horizon forecasting.
  arXiv  Detail & Related papers  (2022-07-16T04:05:15Z)
• Learning summary features of time series for likelihood free inference [93.08098361687722]
  We present a data-driven strategy for automatically learning summary features from time series data. Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values.
  arXiv  Detail & Related papers  (2020-12-04T19:21:37Z)
• Convolutional Tensor-Train LSTM for Spatio-temporal Learning [116.24172387469994]
  We propose a higher-order LSTM model that can efficiently learn long-term correlations in the video sequence. This is accomplished through a novel tensor train module that performs prediction by combining convolutional features across time. Our results achieve state-of-the-art performance-art in a wide range of applications and datasets.
  arXiv  Detail & Related papers  (2020-02-21T05:00:01Z)
• 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.