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Long-Short Term Echo State Network for Time Series Prediction
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DOI：10.1109/ACCESS.2020.2994773.png)
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Abstract
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The Echo State Networks (ESNs) is an efficient recurrent neural network consisting of a randomly generated reservoir (a large number of neurons with sparse random recurrent connections) and a trainable linear layer. It has received widespread attention for its simplicity and effectiveness, especially for time series prediction tasks. However, there is no explicit mechanism in ESNs to capture the inherent multi-scale characteristics of time series. To this end, we propose a model consisting of multi-reservoir structure named long-short term echo state networks (LS-ESNs) to capture the multi-scale temporal characteristics of time series. Specifically, LS-ESNs consists of three independent reservoirs, and each reservoir has recurrent connections of a specific time-scale to model the temporal dependencies of time series. The multi-scale echo states are then collected from each reservoir and concatenated together. Finally, the concatenated echo states representations are fed to the linear regression layer to obtain the results. Experiments on two time series prediction benchmark data sets and a real-world power load data sets demonstrate the effectiveness of the proposed LS-ESNs.
Keywords:
Reservoirs
Time series analysis
Neurons
Benchmark testing
Stacking
History
Mathematical model
Time series prediction
echo state networks (ESNs)
multi-scale temporal dependencies
long short term reservoir
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