Add to ORKGSequences are typically modelled with recurrent architectures, but growing research is finding convolutional architectures to also work well for sequence modelling [1]. We explore the performance of Temporal Convolutional Networks (TCNs) when applied to an important sequence modelling task: solar flare prediction. We take this approach, as our future goal is to apply techniques developed for probing and interpreting general convolutional neural networks (CNNs) to solar flare prediction
In this work we present MTEX-CNN, a novel explainable convolutional neural network architecture whic...
Aims. In this study, we introduce a general paradigm for generating independent and well-balanced tr...
We present a long short-term memory (LSTM) network for predicting whether an active region (AR) woul...
Operational flare forecasting aims at providing predictions that can be used to make decisions, typi...
With recent developments and advances in machine learning methods, traditional time series analysis ...
Solar flares are solar storm events driven by the magnetic field in the solar activity area. Solar f...
When an intense brightness for a small amount of time is seen in the sun, then we can say that a sol...
In this thesis, we develop a collection of deep learning models for time series forecasting. Primary...
MEng (Computer and Electronic Engineering), North-West University, Potchefstroom CampusSolar flares a...
We demonstrate that CNN deep neural networks can not only be used for making predictions based on mu...
The problem of forecasting hourly solar irradiance over a multi-step horizon is dealt with by using ...
Solar flares are releases of electromagnetic energy that occur on the Sun's surface and can reach th...
In this talk, we discuss the application of various machine learning algorithms -- such as Support V...
Despite the advances in the field of solar energy, improvements of solar forecasting techniques, add...
This paper designs a hybridized deep learning framework that integrates the Convolutional Neural Net...
In this work we present MTEX-CNN, a novel explainable convolutional neural network architecture whic...
Aims. In this study, we introduce a general paradigm for generating independent and well-balanced tr...
We present a long short-term memory (LSTM) network for predicting whether an active region (AR) woul...
Operational flare forecasting aims at providing predictions that can be used to make decisions, typi...
With recent developments and advances in machine learning methods, traditional time series analysis ...
Solar flares are solar storm events driven by the magnetic field in the solar activity area. Solar f...
When an intense brightness for a small amount of time is seen in the sun, then we can say that a sol...
In this thesis, we develop a collection of deep learning models for time series forecasting. Primary...
MEng (Computer and Electronic Engineering), North-West University, Potchefstroom CampusSolar flares a...
We demonstrate that CNN deep neural networks can not only be used for making predictions based on mu...
The problem of forecasting hourly solar irradiance over a multi-step horizon is dealt with by using ...
Solar flares are releases of electromagnetic energy that occur on the Sun's surface and can reach th...
In this talk, we discuss the application of various machine learning algorithms -- such as Support V...
Despite the advances in the field of solar energy, improvements of solar forecasting techniques, add...
This paper designs a hybridized deep learning framework that integrates the Convolutional Neural Net...
In this work we present MTEX-CNN, a novel explainable convolutional neural network architecture whic...
Aims. In this study, we introduce a general paradigm for generating independent and well-balanced tr...
We present a long short-term memory (LSTM) network for predicting whether an active region (AR) woul...