References

Below is the list of all studies referenced in this web page.

X

Directly Relevant Studies, Datasets, and Packages

[a] Angryk, R.A., Martens, P.C., Aydin, B., Kempton, D., Mahajan, S.S., Basodi, S., Ahmadzadeh, A., Cai, X., Boubrahimi, S.F., Hamdi, S.M., Schuh, M.A. and Georgoulis, M.K., 2019. Multivariate Time Series Dataset for Space Weather Data Analytics. Sci. Data, Nature, submitted (2019).

[b] Ahmadzadeh, Azim, et al. "Challenges with Extreme Class-Imbalance and Temporal Coherence: A Study on Solar Flare Data." arXiv preprint arXiv:1911.09061 (2019).

[c] Wang, Zhiguang, and Tim Oates. "Encoding time series as images for visual inspection and classification using tiled convolutional neural networks." Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence. 2015. APA

[d] Johann Faouzi and Hicham Janati. pyts: A python package for time series classification. Journal of Machine Learning Research, 21(46):1−6, 2020.

[e] Tavenard, Romain. "tslearn: A machine learning toolkit dedicated to time-series data." URL https://github. com/rtavenar/tslearn (2017).

[f] Cuturi, Marco. "Fast global alignment kernels." Proceedings of the 28th international conference on machine learning (ICML-11). 2011.

A

Deep Learning on Forecasting & Classification

Wang, H. N., et al. "Solar flare forecasting model supported with artificial neural network techniques." Advances in Space Research 42.9 (2008): 1464-1468.
Nishizuka, Naoto, et al. "Deep Flare Net (DeFN) model for solar flare prediction." The Astrophysical Journal 858.2 (2018): 113.
J. Cai, W. Carande, J. Craft, M. Hartnett, A. Jones, K. Kokkonen, T. Morland, and L. Sandoval. Solar flare forecasting: A novel deep learning approach. In AGU Fall Meeting Abstracts, 2018.
Pham, Chau, Vung Pham, and Tommy Dang. "Solar Flare Prediction Using Two-tier Ensemble with Deep Learning and Gradient Boosting Machine." 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019.
Jiao, Zhenbang, et al. "Solar Flare Intensity Prediction with Machine Learning Models." arXiv preprint arXiv:1912.06120 (2019).
Liu, Hao, et al. "Predicting Solar Flares Using a Long Short-term Memory Network." The Astrophysical Journal 877.2 (2019): 121.APA
Sun, Hu, et al. "Interpreting LSTM Prediction on Solar Flare Eruption with Time-series Clustering." arXiv preprint arXiv:1912.12360 (2019).
R. Qahwaji and T. Colak. Automatic short-term solar flare prediction using machine learning and sunspot associations. Solar Physics, 241(1):195–211, 2007.

B

Machine Learning on Forecasting & Classification

K. Leka and G. Barnes. Photospheric magnetic field properties of flaring versus flare-quiet active regions. ii. discriminant analysis. The Astrophysical Journal, 595(2):1296, 2003.
Li, Rong, et al. "Support vector machine combined with k-nearest neighbors for solar flare forecasting." Chinese Journal of Astronomy and Astrophysics 7.3 (2007): 441.
Bloomfield, D. Shaun, et al. "Toward reliable benchmarking of solar flare forecasting methods." The Astrophysical Journal Letters 747.2 (2012): L41.
M. G. Bobra and S. Couvidat. Solar flare prediction using sdo/hmi vector magnetic field data with a machine-learning algorithm. The Astrophysical Journal, 798(2):135, 2015.
N. Nishizuka, K. Sugiura, Y. Kubo, M. Den, S. Watari, and M. Ishii. Solar flare prediction model with three machine-learning algorithms using ultraviolet brightening and vector magnetograms. The Astrophysical Journal, 835(2):156, 2017.
Cinto, T., et al. "Solar Flares Forecasting Using Time Series and Extreme Gradient Boosting Ensembles." arXiv preprint arXiv:2004.13299 (2020).
McGuire, Dan, Renan Sauteraud, and Vishal Midya. "Window-Based Feature Extraction Method Using XGBoost for Time Series Classification of Solar Flares." 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019.
J. Jing, H. Song, V. Abramenko, C. Tan, and H. Wang. The statistical relationship between the photospheric magnetic parameters and the flare productivity of active regions. The Astrophysical Journal, 644(2):1273, 2006
Y. Cui, R. Li, L. Zhang, Y. He, and H. Wang. Correlation between solar flare productivity and photospheric magnetic field properties. Solar Physics, 237(1):45–59, 2006.
H. Song, C. Tan, J. Jing, H. Wang, V. Yurchyshyn, and V. Abramenko. Statistical assessment of photospheric magnetic features in imminent solar flare predictions. Solar Physics, 254(1):101–125, 2009.
A. Al-Ghraibah, L. Boucheron, and R. McAteer. An automated classification approach to ranking photospheric proxies of magnetic energy build-up. Astronomy & Astrophysics, 579:A64, 2015.
H. Song, C. Tan, J. Jing, H. Wang, V. Yurchyshyn, and V. Abramenko. Statistical assessment of photospheric magnetic features in imminent solar flare predictions. Solar Physics, 254(1):101–125, 2009
Murray, Sophie A. "The importance of ensemble techniques for operational space weather forecasting." Space Weather 16.7 (2018): 777-783.
Ben-Hur, Asa, and Jason Weston. "A user’s guide to support vector machines." Data mining techniques for the life sciences. Humana Press, 2010. 223-239.

C

General ML & DL Models

J. H. Friedman. Greedy Function Approximation: A gradient Boosting Machine. The Annals of Statistics, 29(5):1189–1232, 2001.
T. Chen and C. Guestrin. XGBoost. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’16, vol. 19, pp. 785–794. ACM Press, New York, New York, USA, 2016. doi: 10.1145/2939672.2939785
L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin. Catboost: Unbiased boosting with categorical features. Advances in Neural Information Processing Systems, 2018-December(Section 4):6638– 6648, 2018.
A. Rogozhnikov and T. Likhomanenko. InfiniteBoost: building infinite ensembles with gradient descent. pp. 1–7, 2017.
G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T. Y. Liu. LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 2017- December(Nips):3147–3155, 2017

Page updated

Google Sites

Report abuse