A resource-allocating network for function interpolation
Neural Computation
Data Set A is a Pattern Matching Problem
Neural Processing Letters
Evolutionary Neural Networks for Nonlinear Dynamics Modeling
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Local Learning for Iterated Time-Series Prediction
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Recurrent Nets that Time and Count
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
A Bounded Exploration Approach to Constructive Algorithms for Recurrent Neural Networks
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Learning to Forget: Continual Prediction with LSTM
Neural Computation
Learning Chaotic Attractors by Neural Networks
Neural Computation
Neural Computation
Learning long-term dependencies in NARX recurrent neural networks
IEEE Transactions on Neural Networks
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
LSTM recurrent networks learn simple context-free and context-sensitive languages
IEEE Transactions on Neural Networks
Recurrent Neural Networks as Local Models for Time Series Prediction
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Clifford support vector machines for classification, regression, and recurrence
IEEE Transactions on Neural Networks
Time delay learning by gradient descent in recurrent neural networks
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Predicting chaotic time series by boosted recurrent neural networks
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
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Long Short-Term Memory (LSTM) is able to solve many time series tasks unsolvable by feed-forward networks using fixed size time windows. Here we find that LSTM's superiority does not carry over to certain simpler time series prediction tasks solvable by time window approaches: the Mackey-Glass series and the Santa Fe FIR laser emission series (Set A). This suggests to use LSTM only when simpler traditional approaches fail.