cPNN: Continuous Progressive Neural Networks for Evolving Streaming Time Series

• URL: http://arxiv.org/abs/2603.03040v1
• Date: Tue, 03 Mar 2026 14:34:33 GMT
• Title: cPNN: Continuous Progressive Neural Networks for Evolving Streaming Time Series
• Authors: Federico Giannini, Giacomo Ziffer, Emanuele Della Valle,
• Abstract summary: This work proposes Continuous Progressive Neural Networks (cPNN)<n>cPNN tames concept drifts, handles temporal dependencies, and bypasses catastrophic forgetting.<n>We base our method on Recurrent Neural Networks and exploit data streams applied to temporal dependencies.
• Score: 1.3509194648045753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dealing with an unbounded data stream involves overcoming the assumption that data is identically distributed and independent. A data stream can, in fact, exhibit temporal dependencies (i.e., be a time series), and data can change distribution over time (concept drift). The two problems are deeply discussed, and existing solutions address them separately: a joint solution is absent. In addition, learning multiple concepts implies remembering the past (a.k.a. avoiding catastrophic forgetting in Neural Networks' terminology). This work proposes Continuous Progressive Neural Networks (cPNN), a solution that tames concept drifts, handles temporal dependencies, and bypasses catastrophic forgetting. cPNN is a continuous version of Progressive Neural Networks, a methodology for remembering old concepts and transferring past knowledge to fit the new concepts quickly. We base our method on Recurrent Neural Networks and exploit the Stochastic Gradient Descent applied to data streams with temporal dependencies. Results of an ablation study show a quick adaptation of cPNN to new concepts and robustness to drifts.

Related papers

• Echoes of the past: A unified perspective on fading memory and echo states [4.595000276111106]
  Recurrent neural networks (RNNs) have become increasingly popular in information processing tasks involving time series and temporal data.<n>Various notions have been proposed to conceptualize the behavior of memory in RNNs, including steady states, echo states, state forgetting, input forgetting, and fading memory.<n>This work aims to unify these notions in a common language, derive new implications and equivalences between them, and provide alternative proofs to some existing results.
  arXiv  Detail & Related papers  (2025-08-26T15:55:14Z)
• Deep Neural Networks Tend To Extrapolate Predictably [51.303814412294514]
  neural network predictions tend to be unpredictable and overconfident when faced with out-of-distribution (OOD) inputs. We observe that neural network predictions often tend towards a constant value as input data becomes increasingly OOD. We show how one can leverage our insights in practice to enable risk-sensitive decision-making in the presence of OOD inputs.
  arXiv  Detail & Related papers  (2023-10-02T03:25:32Z)
• Time Regularization in Optimal Time Variable Learning [0.4490343701046724]
  Recently, optimal time variable learning in deep neural networks (DNNs) was introduced in arXiv:2204.08528. We extend the concept by introducing a regularization term that directly relates to the time horizon in discrete dynamical systems. We propose an adaptive pruning approach for Residual Neural Networks (ResNets) Results are illustrated by applying the proposed concepts to classification tasks on the well known MNIST and Fashion MNIST data sets.
  arXiv  Detail & Related papers  (2023-06-28T11:27:48Z)
• Recurrent Neural Networks for Learning Long-term Temporal Dependencies with Reanalysis of Time Scale Representation [16.32068729107421]
  We argue that the interpretation of a forget gate as a temporal representation is valid when the gradient of loss with respect to the state decreases exponentially as time goes back. We propose an approach to construct new RNNs that can represent a longer time scale than conventional models.
  arXiv  Detail & Related papers  (2021-11-05T06:22:58Z)
• Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State [66.2457134675891]
  Spiking neural networks (SNNs) are brain-inspired models that enable energy-efficient implementation on neuromorphic hardware. Most existing methods imitate the backpropagation framework and feedforward architectures for artificial neural networks. We propose a novel training method that does not rely on the exact reverse of the forward computation.
  arXiv  Detail & Related papers  (2021-09-29T07:46:54Z)
• Neural ODE Processes [64.10282200111983]
  We introduce Neural ODE Processes (NDPs), a new class of processes determined by a distribution over Neural ODEs. We show that our model can successfully capture the dynamics of low-dimensional systems from just a few data-points.
  arXiv  Detail & Related papers  (2021-03-23T09:32:06Z)
• Overcoming Catastrophic Forgetting in Graph Neural Networks [50.900153089330175]
  Catastrophic forgetting refers to the tendency that a neural network "forgets" the previous learned knowledge upon learning new tasks. We propose a novel scheme dedicated to overcoming this problem and hence strengthen continual learning in graph neural networks (GNNs) At the heart of our approach is a generic module, termed as topology-aware weight preserving(TWP)
  arXiv  Detail & Related papers  (2020-12-10T22:30:25Z)
• Short-Term Memory Optimization in Recurrent Neural Networks by Autoencoder-based Initialization [79.42778415729475]
  We explore an alternative solution based on explicit memorization using linear autoencoders for sequences. We show how such pretraining can better support solving hard classification tasks with long sequences. We show that the proposed approach achieves a much lower reconstruction error for long sequences and a better gradient propagation during the finetuning phase.
  arXiv  Detail & Related papers  (2020-11-05T14:57:16Z)
• Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks [80.15411508088522]
  Spiking neural networks (SNNs) have shown advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency. We propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition.
  arXiv  Detail & Related papers  (2020-07-02T15:38:44Z)
• Fractional Deep Neural Network via Constrained Optimization [0.0]
  This paper introduces a novel algorithmic framework for a deep neural network (DNN) Fractional-DNN can be viewed as a time-discretization of a fractional in time nonlinear ordinary differential equation (ODE)
  arXiv  Detail & Related papers  (2020-04-01T21:58:21Z)
• Depth Enables Long-Term Memory for Recurrent Neural Networks [0.0]
  We introduce a measure of the network's ability to support information flow across time, referred to as the Start-End separation rank. We prove that deep recurrent networks support Start-End separation ranks which are higher than those supported by their shallow counterparts.
  arXiv  Detail & Related papers  (2020-03-23T10:29:14Z)

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.