Temporal Convolution Derived Multi-Layered Reservoir Computing

• URL: http://arxiv.org/abs/2407.06771v2
• Date: Sun, 3 Nov 2024 16:04:06 GMT
• Title: Temporal Convolution Derived Multi-Layered Reservoir Computing
• Authors: Johannes Viehweg, Dominik Walther, Patrick Mäder,
• Abstract summary: We propose a new mapping of input data into the reservoir's state space. We incorporate this method in two novel network architectures increasing parallelizability, depth and predictive capabilities of the neural network. For the chaotic time series, we observe an error reduction of up to $85.45%$ compared to Echo State Networks and $90.72%$ compared to Gated Recurrent Units.
• Score: 5.261277318790788
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The prediction of time series is a challenging task relevant in such diverse applications as analyzing financial data, forecasting flow dynamics or understanding biological processes. Especially chaotic time series that depend on a long history pose an exceptionally difficult problem. While machine learning has shown to be a promising approach for predicting such time series, it either demands long training time and much training data when using deep Recurrent Neural Networks. Alternative, when using a Reservoir Computing approach it comes with high uncertainty and typically a high number of random initializations and extensive hyper-parameter tuning. In this paper, we focus on the Reservoir Computing approach and propose a new mapping of input data into the reservoir's state space. Furthermore, we incorporate this method in two novel network architectures increasing parallelizability, depth and predictive capabilities of the neural network while reducing the dependence on randomness. For the evaluation, we approximate a set of time series from the Mackey-Glass equation, inhabiting non-chaotic as well as chaotic behavior as well as the SantaFe Laser dataset and compare our approaches in regard to their predictive capabilities to Echo State Networks, Autoencoder connected Echo State Networks and Gated Recurrent Units. For the chaotic time series, we observe an error reduction of up to $85.45\%$ compared to Echo State Networks and $90.72\%$ compared to Gated Recurrent Units. Furthermore, we also observe tremendous improvements for non-chaotic time series of up to $99.99\%$ in contrast to the existing approaches.

Related papers

• Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks [1.5281057386824812]
  This work introduces Neural Galerkin schemes that update randomized sparse subsets of network parameters at each time step. The randomization avoids overfitting locally in time and so helps prevent the error from accumulating quickly over the sequential-in-time training. In numerical experiments with a wide range of evolution equations, the proposed scheme with randomized sparse updates is up to two orders of magnitude more accurate at a fixed computational budget.
  arXiv  Detail & Related papers  (2023-10-07T16:27:00Z)
• STING: Self-attention based Time-series Imputation Networks using GAN [4.052758394413726]
  STING (Self-attention based Time-series Imputation Networks using GAN) is proposed. We take advantage of generative adversarial networks and bidirectional recurrent neural networks to learn latent representations of the time series. Experimental results on three real-world datasets demonstrate that STING outperforms the existing state-of-the-art methods in terms of imputation accuracy.
  arXiv  Detail & Related papers  (2022-09-22T06:06:56Z)
• 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)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• Time Series Forecasting with Ensembled Stochastic Differential Equations Driven by L\'evy Noise [2.3076895420652965]
  We use a collection of SDEs equipped with neural networks to predict long-term trend of noisy time series. Our contributions are, first, we use the phase space reconstruction method to extract intrinsic dimension of the time series data. Second, we explore SDEs driven by $alpha$-stable L'evy motion to model the time series data and solve the problem through neural network approximation.
  arXiv  Detail & Related papers  (2021-11-25T16:49:01Z)
• Novel Features for Time Series Analysis: A Complex Networks Approach [62.997667081978825]
  Time series data are ubiquitous in several domains as climate, economics and health care. Recent conceptual approach relies on time series mapping to complex networks. Network analysis can be used to characterize different types of time series.
  arXiv  Detail & Related papers  (2021-10-11T13:46:28Z)
• Bilinear Input Normalization for Neural Networks in Financial Forecasting [101.89872650510074]
  We propose a novel data-driven normalization method for deep neural networks that handle high-frequency financial time-series. The proposed normalization scheme takes into account the bimodal characteristic of financial time-series. Our experiments, conducted with state-of-the-arts neural networks and high-frequency data, show significant improvements over other normalization techniques.
  arXiv  Detail & Related papers  (2021-09-01T07:52:03Z)
• Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information [52.635997570873194]
  This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information. The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
  arXiv  Detail & Related papers  (2021-01-12T20:08:18Z)
• Improved Predictive Deep Temporal Neural Networks with Trend Filtering [22.352437268596674]
  We propose a new prediction framework based on deep neural networks and a trend filtering. We reveal that the predictive performance of deep temporal neural networks improves when the training data is temporally processed by a trend filtering.
  arXiv  Detail & Related papers  (2020-10-16T08:29:36Z)
• Multivariate Time Series Classification Using Spiking Neural Networks [7.273181759304122]
  Spiking neural network has drawn attention as it enables low power consumption. We present an encoding scheme to convert time series into sparse spatial temporal spike patterns. A training algorithm to classify spatial temporal patterns is also proposed.
  arXiv  Detail & Related papers  (2020-07-07T15:24:01Z)
• Liquid Time-constant Networks [117.57116214802504]
  We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a learning system's dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations.
  arXiv  Detail & Related papers  (2020-06-08T09:53:35Z)

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.