Detecting Methane Outbreaks from Time Series Data with Deep Neural Networks

2015 ◽  
pp. 475-484 ◽  
Author(s):  
Krzysztof Pawłowski ◽  
Karol Kurach
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Neural Networks ◽  
Time Series Data ◽  
Series Data

scholarly journals Explainable Deep Neural Networks for Multivariate Time Series Predictions

2019 ◽  
Author(s):  
Roy Assaf ◽  
Anika Schumann
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Network Architecture ◽  
Power Plants ◽  
Deep Neural Networks ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Average Energy ◽  
Series Data ◽  
Time Interval

We demonstrate that CNN deep neural networks can not only be used for making predictions based on multivariate time series data, but also for explaining these predictions. This is important for a number of applications where predictions are the basis for decisions and actions. Hence, confidence in the prediction result is crucial. We design a two stage convolutional neural network architecture which uses particular kernel sizes. This allows us to utilise gradient based techniques for generating saliency maps for both the time dimension and the features. These are then used for explaining which features during which time interval are responsible for a given prediction, as well as explaining during which time intervals was the joint contribution of all features most important for that prediction. We demonstrate our approach for predicting the average energy production of photovoltaic power plants and for explaining these predictions.

Generating Healthcare Time Series Data for Improving Diagnostic Accuracy of Deep Neural Networks

2021 ◽  
pp. 1-1
Author(s):  
Barbara Mukami Maweu ◽  
Rittika Shamsuddin ◽  
Sagnik Dakshit ◽  
Balakrishnan Prabhakaran
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Diagnostic Accuracy ◽  
Deep Neural Networks ◽  
Time Series Data ◽  
Series Data

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

A review of irregular time series data handling with gated recurrent neural networks

2021 ◽  
Vol 441 ◽  
pp. 161-178
Author(s):  
Philip B. Weerakody ◽  
Kok Wai Wong ◽  
Guanjin Wang ◽  
Wendell Ela
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Time Series Data ◽  
Series Data ◽  
Data Handling

scholarly journals Ship-Collision Avoidance Decision-Making Learning of Unmanned Surface Vehicles with Automatic Identification System Data Based on Encoder—Decoder Automatic-Response Neural Networks

2020 ◽  
Vol 8 (10) ◽  
pp. 754
Author(s):  
Miao Gao ◽  
Guo-You Shi
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Time Series ◽  
Collision Avoidance ◽  
Time Series Data ◽  
Series Data ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Response ◽  
Ship Collision

Intelligent unmanned surface vehicle (USV) collision avoidance is a complex inference problem based on current navigation status. This requires simultaneous processing of the input sequences and generation of the response sequences. The automatic identification system (AIS) encounter data mainly include the time-series data of two AIS sets, which exhibit a one-to-one mapping relation. Herein, an encoder–decoder automatic-response neural network is designed and implemented based on the sequence-to-sequence (Seq2Seq) structure to simultaneously process the two AIS encounter trajectory sequences. Furthermore, this model is combined with the bidirectional long short-term memory recurrent neural networks (Bi-LSTM RNN) to obtain a network framework for processing the time-series data to obtain ship-collision avoidance decisions based on big data. The encoder–decoder neural networks were trained based on the AIS data obtained in 2018 from Zhoushan Port to achieve ship collision avoidance decision-making learning. The results indicated that the encoder–decoder neural networks can be used to effectively formulate the sequence of the collision avoidance decision of the USV. Thus, this study significantly contributes to the increased efficiency and safety of maritime transportation. The proposed method can potentially be applied to the USV technology and intelligent collision-avoidance systems.

An Image Analysis Algorithm for Dendritic Spines

2002 ◽  
Vol 14 (6) ◽  
pp. 1283-1310 ◽  
Author(s):  
Ingrid Y. Y. Koh ◽  
W. Brent Lindquist ◽  
Karen Zito ◽  
Esther A. Nimchinsky ◽  
Karel Svoboda
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Dendritic Spines ◽  
Dendritic Spine ◽  
Time Series Data ◽  
Neuronal Morphology ◽  
Laser Scanning Microscopy ◽  
Series Data ◽  
Spine Length ◽  
Spine Morphology

The structure of neuronal dendrites and their spines underlie the connectivity of neural networks. Dendrites, spines, and their dynamics are shaped by genetic programs as well as sensory experience. Dendritic structures and dynamics may therefore be important predictors of the function of neural networks. Based on new imaging approaches and increases in the speed of computation, it has become possible to acquire large sets of high-resolution optical micrographs of neuron structure at length scales small enough to resolve spines. This advance in data acquisition has not been accompanied by comparable advances in data analysis techniques; the analysis of dendritic and spine morphology is still accomplished largely manually. In addition to being extremely time intensive, manual analysis also introduces systematic and hard-to-characterize biases. We present a geometric approach for automatically detecting and quantifying the three-dimensional structure of dendritic spines from stacks of image data acquired using laser scanning microscopy. We present results on the measurement of dendritic spine length, volume, density, and shape classification for both static and timelapse images of dendrites of hippocampal pyramidal neurons. For spine length and density, the automated measurements in static images are compared with manual measurements. Comparisons are also made between automated and manual spine length measurements for a time-series data set. The algorithm performs well compared to a human analyzer, especially on time-series data. Automated analysis of dendritic spine morphology will enable objective analysis of large morphological data sets. The approaches presented here are generalizable to other aspects of neuronal morphology.

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