Add to ORKGIn this paper, a methodology for assessing the unpredictability of systems with memory was developed. The proposed approach consists in approximating the probability distribution exhibited by the response of a system, understood as a stochastic process, with a deep recurrent neural network; such networks offer increased forecasting capability by exploiting an accumulative register of previous system states. Once the probability distribution is computed, the uncertainty or entropy of the underlying process is measured. This measure determines the degree of regularity in the system, and identifies how atypical the system dynamics are. The proposed model was validated by identifying industrial gas turbine engine faults from recorded sensor dat...
International audienceFor dealing with uncertainty in Remaining Useful Life (RUL) predictions, numer...
Time series data often involves big size environment that lead to high dimensionality problem. Many ...
In this paper, we present the exploitation of a method to extract information from microscopic sampl...
Measuring the predictability and complexity of time series using entropy is essential tool designing...
Entropy, as a complexity measure, has been widely applied for time series analysis. One preeminent e...
Chaotic systems are known for their unpredictability due to their sensitive dependence on initial co...
Recurrent neural networks have recently proved the state-of-the-art approach in forecasting complex ...
Recurrent neural networks have recently proved the state-of-the-art approach in forecasting complex ...
Recurrent neural networks have recently proved the state-of-the-art approach in forecasting complex ...
Entropy models the added information associated to data uncertainty, proving that stochasticity is n...
Recurrent neural networks have recently proved the state-of-the-art approach in forecasting complex ...
Recurrent neural networks have recently proved the state-of-the-art approach in forecasting complex ...
Recurrent neural networks have recently proved the state-of-the-art approach in forecasting complex ...
An echo state network (ESN) is an efficient recurrent neural network (RNN) that is widely used in ti...
Entropy, as a complexity measure, has been widely applied for time series analysis. One preeminent e...
International audienceFor dealing with uncertainty in Remaining Useful Life (RUL) predictions, numer...
Time series data often involves big size environment that lead to high dimensionality problem. Many ...
In this paper, we present the exploitation of a method to extract information from microscopic sampl...
Measuring the predictability and complexity of time series using entropy is essential tool designing...
Entropy, as a complexity measure, has been widely applied for time series analysis. One preeminent e...
Chaotic systems are known for their unpredictability due to their sensitive dependence on initial co...
Recurrent neural networks have recently proved the state-of-the-art approach in forecasting complex ...
Recurrent neural networks have recently proved the state-of-the-art approach in forecasting complex ...
Recurrent neural networks have recently proved the state-of-the-art approach in forecasting complex ...
Entropy models the added information associated to data uncertainty, proving that stochasticity is n...
Recurrent neural networks have recently proved the state-of-the-art approach in forecasting complex ...
Recurrent neural networks have recently proved the state-of-the-art approach in forecasting complex ...
Recurrent neural networks have recently proved the state-of-the-art approach in forecasting complex ...
An echo state network (ESN) is an efficient recurrent neural network (RNN) that is widely used in ti...
Entropy, as a complexity measure, has been widely applied for time series analysis. One preeminent e...
International audienceFor dealing with uncertainty in Remaining Useful Life (RUL) predictions, numer...
Time series data often involves big size environment that lead to high dimensionality problem. Many ...
In this paper, we present the exploitation of a method to extract information from microscopic sampl...