Deterministic Reservoir Computing for Chaotic Time Series Prediction
Reservoir Computing was shown in recent years to be useful as efficient to learn networks in the field of time series tasks. Their randomized initialization, a computational benefit, results in drawbacks in theoretical analysis of large random graphs, because of which deterministic variations are an still open field of research. Building upon Next-Gen Reservoir Computing and the Temporal Convolution Derived Reservoir Computing, we propose a deterministic alternative to the higher-dimensional mapping therein, TCRC-LM and TCRC-CM, utilizing the parametrized but deterministic Logistic mapping and Chebyshev maps. To further enhance the predictive capabilities in the task of time series forecasting, we propose the novel utilization of the Lobachevsky function as non-linear activation function. As a result, we observe a new, fully deterministic network being able to outperform TCRCs and classical Reservoir Computing in the form of the prominent Echo State Networks by up to $99.99\%$ for the non-chaotic time series and $87.13\%$ for the chaotic ones.
Code (0)
등록된 구현이 없습니다.
Tasks
PredictionTime SeriesTime Series ForecastingTime Series PredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Attention-Enhanced Reservoir Computing
Photonic reservoir computing has been successfully utilized in time-series prediction as the need for hardware implementations has increased. Prediction of chaotic time series remains a significant challenge, an area whe…
PredictionTemporal SequencesTime SeriesTime Series Forecasting+1Effect of temporal resolution on the reproduction of chaotic dynamics via reservoir computing
Reservoir computing is a machine learning paradigm that uses a structure called a reservoir, which has nonlinearities and short-term memory. In recent years, reservoir computing has expanded to new functions such as the …
Time SeriesTime Series AnalysisTime Series PredictionOscillations enhance time-series prediction in reservoir computing with feedback
Reservoir computing, a machine learning framework used for modeling the brain, can predict temporal data with little observations and minimal computational resources. However, it is difficult to accurately reproduce the …
Time SeriesTime Series PredictionReservoir Computing Using Complex Systems
Reservoir Computing is an emerging machine learning framework which is a versatile option for utilising physical systems for computation. In this paper, we demonstrate how a single node reservoir, made of a simple electr…
Time SeriesTime Series AnalysisMachine-learning inference of fluid variables from data using reservoir computing
We infer both microscopic and macroscopic behaviors of a three-dimensional chaotic fluid flow using reservoir computing. In our procedure of the inference, we assume no prior knowledge of a physical process of a fluid fl…
BIG-bench Machine LearningTime SeriesTime Series Analysis