A Deep Learning Approach to Dst Index Prediction

2022-05-05 · Yasser Abduallah, Jason T. L. Wang, Prianka Bose, Genwei Zhang, Firas Gerges, Haimin Wang

The disturbance storm time (Dst) index is an important and useful measurement in space weather research. It has been used to characterize the size and intensity of a geomagnetic storm. A negative Dst value means that the Earth's magnetic field is weakened, which happens during storms. In this paper, we present a novel deep learning method, called the Dst Transformer, to perform short-term, 1-6 hour ahead, forecasting of the Dst index based on the solar wind parameters provided by the NASA Space Science Data Coordinated Archive. The Dst Transformer combines a multi-head attention layer with Bayesian inference, which is capable of quantifying both aleatoric uncertainty and epistemic uncertainty when making Dst predictions. Experimental results show that the proposed Dst Transformer outperforms related machine learning methods in terms of the root mean square error and R-squared. Furthermore, the Dst Transformer can produce both data and model uncertainty quantification results, which can not be done by the existing methods. To our knowledge, this is the first time that Bayesian deep learning has been used for Dst index forecasting.

📄 PDF Abstract BibTeX arXiv:2205.02447

Code (0)

등록된 구현이 없습니다.

Tasks

Bayesian InferenceDeep LearningPredictionUncertainty Quantification

Methods 이 논문이 사용한 방법론

DST Dynamic sparse training methods train neural networks in a sparse manner, starting with an initial sparse mask, and periodically updating the mask based on some criteria.

Attention 설명 없음

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…

BPE Byte Pair Encoding, or BPE, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word…

Absolute Position Encodings Absolute Position Encodings are a type of position embeddings for [Transformer-based models] where positional encodings are…

Residual Connection 설명 없음

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

Fast Computation of Katz Index for Efficient Processing of Link Prediction Queries

2019-12-13 · Mustafa Coskun, Abdelkader Baggag, Mehmet Koyuturk

Network proximity computations are among the most common operations in various data mining applications, including link prediction and collaborative filtering. A common measure of network proximity is Katz index, which h…

Collaborative Filtering Link Prediction Prediction

Conditioning LSTM Decoder and Bi-directional Attention Based Question Answering System

2019-05-02 · Heguang Liu

Applying neural-networks on Question Answering has gained increasing popularity in recent years. In this paper, I implemented a model with Bi-directional attention flow layer, connected with a Multi-layer LSTM encoder, c…

Decoder Question Answering

Early Concept Drift Detection via Prediction Uncertainty

2024-12-15 · Pengqian Lu, Jie Lu, Anjin Liu, Guangquan Zhang

Concept drift, characterized by unpredictable changes in data distribution over time, poses significant challenges to machine learning models in streaming data scenarios. Although error rate-based concept drift detectors…

Drift Detection Prediction

Joint Optimization of Tree-based Index and Deep Model for Recommender Systems

2019-02-19 · NeurIPS 2019 12 · Han Zhu, Daqing Chang, Ziru Xu, Pengye Zhang 외

Large-scale industrial recommender systems are usually confronted with computational problems due to the enormous corpus size. To retrieve and recommend the most relevant items to users under response time limits, resort…

Prediction Recommendation Systems Retrieval

Forecasting with Deep Learning: S&P 500 index

2021-03-21 · Firuz Kamalov, Linda Smail, Ikhlaas Gurrib

Stock price prediction has been the focus of a large amount of research but an acceptable solution has so far escaped academics. Recent advances in deep learning have motivated researchers to apply neural networks to sto…

Deep Learning Prediction Stock Prediction Stock Price Prediction