Untitled

Frank, Ray J.; Davey, Neil; Hunt, Stephen P.

doi:10.1023/a:1012074215150

Cited by 250 publications

(27 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CCNN is a learning algorithm that is proven to provide efficient performance in applications that involve classification and time-series prediction (Frank et al 2001). It has been shown in (Qahwaji and Colak, 2006) that CCNN is the optimal neural network learning algorithm for solar flare prediction using sunspot properties.…”

Section: Machine Learningmentioning

confidence: 99%

Solar Flare Prediction Using Advanced Feature Extraction, Machine Learning, and Feature Selection

et al. 2011

View full text Add to dashboard Cite

Citation: Ahmed OW, Qahwaji RSR, Colak T, Higgins PAB, Gallagher P and Bloomfield S (2013) Solar flare prediction using advanced feature extraction, machine learning and feature selection. Solar Physics. 283(1): 157-175. Abstract: Novel machine-learning and feature-selection algorithms have been developed to study: (i) the flare prediction capability of magnetic feature (MF) properties generated by the recently developed Solar Monitor Active Region Tracker (SMART); (ii) SMART's MF properties that are most significantly related to flare occurrence. Spatio-temporal association algorithms are developed to associate MFs with flares from April 1996 to December 2010 in order to differentiate flaring and non-flaring MFs and enable the application of machine learning and feature selection algorithms. A machine-learning algorithm is applied to the associated datasets to determine the flare prediction capability of all 21 SMART MF properties. The prediction performance is assessed using standard forecast verification measures and compared with the prediction measures of one of the industry's standard technologies for flare prediction that is also based on machine learning -Automated Solar Activity Prediction (ASAP). The comparison shows that the combination of SMART MFs with machine learning has the potential to achieve more accurate flare prediction than ASAP. Feature selection algorithms are then applied to determine the MF properties that are most related to flare occurrence. It is found that a reduced set of 6 MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties. ABSTRACT:Novel machine-learning and feature-selection algorithms have been developed to study: (i) the flare prediction capability of magnetic feature (MF) properties generated by the recently developed Solar Monitor Active Region Tracker (SMART); (ii) SMART's MF properties that are most significantly related to flare occurrence. Spatio-temporal association algorithms are developed to associate MFs with flares from April 1996 to December 2010 in order to differentiate flaring and non-flaring MFs and enable the application of machine learning and feature selection algorithms. A machine-learning algorithm is applied to the associated datasets to determine the flare prediction capability of all 21 SMART MF properties. The prediction performance is assessed using standard forecast verification measures and compared with the prediction measures of one of the industry's standard technologies for flare prediction that is also based on machine learning -Automated Solar Activity Prediction (ASAP). The comparison shows that the combination of SMART MFs with machine learning has the potential to achieve more accurate flare prediction than ASAP. Feature selection algorithms are then applied to determine the MF properties that are most related to flare occurrence. It is found that a reduced set of 6 MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties.

show abstract

Section: Machine Learningmentioning

confidence: 99%

Solar Flare Prediction Using Advanced Feature Extraction, Machine Learning, and Feature Selection

et al. 2011

View full text Add to dashboard Cite

show abstract

“…Because the selection of relevant features is conditioned by the window size (Frank et al 2001) and the feature selection threshold, a window size of 18 was chosen along with a feature selection threshold equal to 65. This configuration yields the best results for all performance metrics.…”

Section: Resultsmentioning

confidence: 99%

Automated early detection of drops in commercial egg production using neural networks

Ramírez-Morales

Fernández-Blanco

Rivero

2017

British Poultry Science

View full text Add to dashboard Cite

ABSTRACT1. The purpose of this work was to support decision-making in poultry farms by performing automatic early detection of anomalies in egg production. 2. Unprocessed data were collected from a commercial egg farm on a daily basis over 7 years. Records from a total of 24 flocks, each with approximately 20 000 laying hens, were studied. 3. Other similar works have required a prior feature extraction by a poultry expert, and this method is dependent on time and expert knowledge. 4. The present approach reduces the dependency on time and expert knowledge because of the automatic selection of relevant features and the use of artificial neural networks capable of costsensitive learning. 5. The optimum configuration of features and parameters in the proposed model was evaluated on unseen test data obtained by a repeated cross-validation technique. 6. The accuracy, sensitivity, specificity and positive predictive value are presented and discussed at 5 forecasting intervals. The accuracy of the proposed model was 0.9896 for the day before a problem occurs.

show abstract

“…Financial instruments such as stocks, indices, prices, auctions and foreign exchange rates can be predicted as time series (Ravi et al, 2012). Considering only previous values, new values are often predicted using any prediction technique (Frank et al, 2001;Schwaerzel and Bylander, 2006;Sheta and Jong, 2001;Yao and Tan, 2000).…”

Section: Jcsmentioning

confidence: 99%

Event-Driven Business Intelligence Approach for Real-Time Integration of Technical and Fundamental Analysis in Forex Market

Abuhamad

Mohd

Salim

2013

Journal of Computer Science

View full text Add to dashboard Cite

Forex market is the most liquid financial market and the largest market in the world. Forex market has been analysed using two isolated approaches, technical analysis and fundamental analysis. Technical analysis attempts to predict the movement of prices by studying the historical data of the market whereas fundamental analysis concerns essentially with the overall state of the economy. Relying on one kind of analysis limits the quality of trading decisions therefore traders usually gain insight into the market by analysing many factors which may influence the market state and the price movement. This process has become increasingly challenging due to the vast and variant number of prices' determinants and the rapid changes in the market dynamics. This study proposes an event-driven business intelligence approach to respond immediately to any change in the market status by generating trading signals based on different analyses. Targeting the value associated with the data as it arrives, different models are built to capture and process the data of three currencies against US dollar in different frequency as well as the data of nine US macroeconomic indicators. The time-series data for both technical and fundamental indicators are modelled using artificial neural network while a knowledge base model is implemented to integrate the signals generated by time-series models. The experimental results show a remarkable improvement in the quality of trading signals using real-time consideration of different analyses.

show abstract

Untitled

Cited by 250 publications

References 2 publications

Solar Flare Prediction Using Advanced Feature Extraction, Machine Learning, and Feature Selection

Solar Flare Prediction Using Advanced Feature Extraction, Machine Learning, and Feature Selection

Automated early detection of drops in commercial egg production using neural networks

Event-Driven Business Intelligence Approach for Real-Time Integration of Technical and Fundamental Analysis in Forex Market

Contact Info

Product

Resources

About