A Meta-learning Approach to Reservoir Computing: Time Series Prediction
with Limited Data
- URL: http://arxiv.org/abs/2110.03722v1
- Date: Thu, 7 Oct 2021 18:23:14 GMT
- Title: A Meta-learning Approach to Reservoir Computing: Time Series Prediction
with Limited Data
- Authors: Daniel Canaday, Andrew Pomerance, and Michelle Girvan
- Abstract summary: We present a data-driven approach to automatically extract an appropriate model structure from experimentally observed processes.
We demonstrate our approach on a simple benchmark problem, where it beats the state of the art meta-learning techniques.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent research has established the effectiveness of machine learning for
data-driven prediction of the future evolution of unknown dynamical systems,
including chaotic systems. However, these approaches require large amounts of
measured time series data from the process to be predicted. When only limited
data is available, forecasters are forced to impose significant model structure
that may or may not accurately represent the process of interest. In this work,
we present a Meta-learning Approach to Reservoir Computing (MARC), a
data-driven approach to automatically extract an appropriate model structure
from experimentally observed "related" processes that can be used to vastly
reduce the amount of data required to successfully train a predictive model. We
demonstrate our approach on a simple benchmark problem, where it beats the
state of the art meta-learning techniques, as well as a challenging chaotic
problem.
Related papers
- Tailored Forecasting from Short Time Series via Meta-learning [0.0]
We introduce Meta-learning for Tailored Forecasting from Related Time Series (METAFORS)
By leveraging a library of models trained on related systems, METAFORS builds tailored models to forecast system evolution with limited data.
We demonstrate METAFORS' ability to predict both short-term dynamics and long-term statistics, even when test and related systems exhibit significantly different behaviors.
arXiv Detail & Related papers (2025-01-27T18:58:04Z) - Capturing the Temporal Dependence of Training Data Influence [100.91355498124527]
We formalize the concept of trajectory-specific leave-one-out influence, which quantifies the impact of removing a data point during training.
We propose data value embedding, a novel technique enabling efficient approximation of trajectory-specific LOO.
As data value embedding captures training data ordering, it offers valuable insights into model training dynamics.
arXiv Detail & Related papers (2024-12-12T18:28:55Z) - Topological Approach for Data Assimilation [0.4972323953932129]
We introduce a new data assimilation algorithm with a foundation in topological data analysis.
By leveraging the differentiability of functions of persistence, gradient descent optimization is used to minimize topological differences between measurements and forecast predictions.
arXiv Detail & Related papers (2024-11-12T20:24:46Z) - Attribute-to-Delete: Machine Unlearning via Datamodel Matching [65.13151619119782]
Machine unlearning -- efficiently removing a small "forget set" training data on a pre-divertrained machine learning model -- has recently attracted interest.
Recent research shows that machine unlearning techniques do not hold up in such a challenging setting.
arXiv Detail & Related papers (2024-10-30T17:20:10Z) - Towards Learning Stochastic Population Models by Gradient Descent [0.0]
We show that simultaneous estimation of parameters and structure poses major challenges for optimization procedures.
We demonstrate accurate estimation of models but find that enforcing the inference of parsimonious, interpretable models drastically increases the difficulty.
arXiv Detail & Related papers (2024-04-10T14:38:58Z) - PILOT: A Pre-Trained Model-Based Continual Learning Toolbox [65.57123249246358]
This paper introduces a pre-trained model-based continual learning toolbox known as PILOT.
On the one hand, PILOT implements some state-of-the-art class-incremental learning algorithms based on pre-trained models, such as L2P, DualPrompt, and CODA-Prompt.
On the other hand, PILOT fits typical class-incremental learning algorithms within the context of pre-trained models to evaluate their effectiveness.
arXiv Detail & Related papers (2023-09-13T17:55:11Z) - Automated Machine Learning Techniques for Data Streams [91.3755431537592]
This paper surveys the state-of-the-art open-source AutoML tools, applies them to data collected from streams, and measures how their performance changes over time.
The results show that off-the-shelf AutoML tools can provide satisfactory results but in the presence of concept drift, detection or adaptation techniques have to be applied to maintain the predictive accuracy over time.
arXiv Detail & Related papers (2021-06-14T11:42:46Z) - Using Data Assimilation to Train a Hybrid Forecast System that Combines
Machine-Learning and Knowledge-Based Components [52.77024349608834]
We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements.
We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
arXiv Detail & Related papers (2021-02-15T19:56:48Z) - Model-Based Deep Learning [155.063817656602]
Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques.
Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance.
We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
arXiv Detail & Related papers (2020-12-15T16:29:49Z) - Supervised learning from noisy observations: Combining machine-learning
techniques with data assimilation [0.6091702876917281]
We show how to optimally combine forecast models and their inherent uncertainty with incoming noisy observations.
We show that the obtained forecast model has remarkably good forecast skill while being computationally cheap once trained.
Going beyond the task of forecasting, we show that our method can be used to generate reliable ensembles for probabilistic forecasting as well as to learn effective model closure in multi-scale systems.
arXiv Detail & Related papers (2020-07-14T22:29:37Z)
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.