Super Forecasting The Art And Science Of Prediction Epub 30 |LINK|
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Hopefully, this paper brings more clarity on which ensembles techniques is best suitable for machine learning tasks in stock market prediction. Again, offer help to beginners in the machine-learning field, to make an informed choice concerning ensemble methods that quickly offer best and accurate results in stock-market prediction. Furthermore, we probe the arguments made in [12, 21] about the consistency of ensemble learning superiority over stock data from different countries. Finally, this paper contributes to the literature in that it is, to the best of our knowledge, the first in stock market prediction to make such an extensive comparative analysis of ensemble techniques.
A Comparison of single, ensemble and integrated ensemble ML techniques to predict the stock market was carried out in . The study showed that boosting ensemble classifiers outperformed bagged classifiers. Sun et al.  proposed an ensemble LSTM using AdaBoost for stock market prediction. Their results show that the proposed AdaBoost-LSTM ensemble outperformed some other single forecasting models. A homogenous ensemble of time-series models including SVM, logistic regression, Lasso regression, polynomial regression, Naive forecast and more was proposed in  for predicting stock price movement. Likewise, Yang et al.  ensembled SVM, RF and AdaBoost using voting techniques to predict a buy or sell of stocks for intraday, weekly and monthly. The study shows that the ensemble technique outperformed single classifier in terms of accuracy. Gan et al.  proposed an ensemble of feedforward neural networks for predicting the stock closing price and reported a higher accuracy in prediction as compared with single feedforward neural networks.
Currently, flare prediction is tackled by the following four approaches: (i) empirical human forecasting (Crown 2012; Devos et al. 2014; Kubo et al. 2017; Murray et al. 2017), (ii) statistical prediction methods (Lee et al. 2012; Bloomfield et al. 2012; McCloskey et al. 2016; Leka et al. 2018), (iii) machine learning methods (e.g., Bobra and Couvidat 2015; Muranushi et al. 2015; Nishizuka et al. 2017, and references therein), and (iv) numerical simulations based on physics equations (e.g., Kusano et al. 2012, 2020; Inoue et al. 2018; Korsós et al. 2020). Some of the models have been made available for community use at the Community Coordinated Modeling Center (CCMC) of NASA (e.g., Gallagher et al. 2002; Shih and Kowalsky 2003; Colak and Qahwaji 2008, 2009; Krista and Gallagher 2009; Steward et al. 2011; Falconer et al. 2011, 2012). It is useful to show the robust performance of each model, and in benchmark workshops, prediction models were evaluated for comparison, where methods that included machine learning algorithms as part of their system were also discussed (Barnes et al. 2016; Leka 2019; Park 2020).
Recently, the application of supervised machine learning methods, especially deep neural networks (DNNs), to solar flare prediction has been a hot topic, and their successful application in research has been reported (Huang et al. 2018; Nishizuka et al. 2018; Park et al. 2018; Chen et al. 2019; Domijan et al. 2019; Liu et al. 2019; Zheng et al. 2019; Bhattacharjee et al. 2020; Jiao et al. 2020; Li et al. 2020; Panos and Kleint 2020; Yi et al. 2020). However, there is insufficient discussion on how to develop the methods available to real-time operations in space weather forecasting offices, including the methods for validation and verification of the models. Currently, new physical and geometrical (topological) features are applied to flare prediction using machine learning (e.g., Wang et al. 2020a; Deshmukh et al. 2020), and it has been noted that training sets may be sensitive to which period in the solar cycle they are drawn from. (Wang et al. 2020b).
Here, we propose the use of time-series CV for evaluations of operational forecasting models. In the previous papers on flare predictions, we used hold-out CV, where a subset of the data split chronologically was reserved for validation and testing, rather than the naïve K-fold CV. This is because it is necessary to be careful when splitting the time-series data to prevent data leakage (Nishizuka et al. 2018). To accurately evaluate prediction models in an operational setting, we must not use all the data about events that occur chronologically after the events used for training.
The time-series CV is illustrated in Fig. 4. In this procedure, there are a series of testing datasets, each consisting of a set of observations and used for prediction error. The corresponding training dataset consists of observations that occurred prior to the observations that formed the testing dataset and is used for parameter tuning. Thus, model testing is not done on data that may have pre-dated the training set. Furthermore, the training dataset is divided into training and validation datasets. The model prediction accuracy is calculated by averaging over the testing datasets. This procedure is called rolling forecasting origin-based CV (Tashman 2000). In this paper, we call it time-series CV, and it provides an almost unbiased estimate of the true error (Varma and Simon 2006).
In this paper, we showed contingency tables of our prediction results. No matter how many skill scores you show, you will not have more information than one contingency table. We evaluated our prediction results as a deterministic forecasting model. The ROC curve and the reliability diagram, which are shown in Barnes et al. (2016) and Leka et al. (2019), can also be reproduced from the contingency table if it is related to the deterministic forecast.
We demonstrated the performance of a machine learning model in an operational flare forecasting scenario. The same methods and discussion of prediction using machine learning algorithms can be applied to other forecasting models of space weather in the magnetosphere and ionosphere. Our future aim is to extend our model to predicting CMEs and social impacts on Earth by extending our database to include geoeffective phenomena and technological infrastructures. 2b1af7f3a8