Machine Learning Techniques for Deciphering Implied Volatility Surface Data in a Hostile Environment: Scenario Based Particle Filter, Risk Factor Decomposition & Arbitrage Constraint Sampling

2018 ◽  
Author(s):  
Babak Mahdavi-Damghani ◽  
Stephen Roberts
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Implied Volatility ◽  
Machine Learning Techniques ◽  
Hostile Environment ◽  
Implied Volatility Surface ◽  
Surface Data ◽  
Learning Techniques ◽  
Factor Decomposition ◽  
Volatility Surface

Predict depth constraints for lithospheric modelling by machine learning 

2021 ◽  
Author(s):  
Nils Holzrichter ◽  
Alexandra Guy ◽  
Jörg Ebbing
Keyword(s):  
Machine Learning ◽  
Additional Data ◽  
Machine Learning Techniques ◽  
Potential Field Data ◽  
Central Asian ◽  
Surface Data ◽  
Learning Techniques ◽  
Machine Learning Applications ◽  
Data Coverage ◽  
Surface Geology

<p>Machine learning applications in geophysical studies are often used to predict geophysical observations in areas with sparse or not data or recognize patterns and similarities in data. In our study, we test different techniques to improve the information of constraining data by machine learning and to improve strengthen the modelling of lithospheric structures with potential field data. Constraining data like seismic information, surface geology, rock classifications etc. is often used during the interpretation step of lithospheric modelling to aid the qualitative interpretation. Consensus between additional data and the own model is assessed by comparison and used to describe the model goodness consistency. First Wwe test, how this additional data can be used before the modelling by using machine learning techniques to quantify the data. We focus on supervised learning to predict crustal structure in areas with little constraints, on trained learning in data-rich areas. Second, we test the spatial analysis of surface data to determine lithospheric boundaries in depth. These tests are performed in North America and the Central Asian Orogenic belt (CAOB) to compare the results in areas with respectively good and spare data coverage. That approach can be used to link the large variety of surface and deep information in the CAOB region.</p><p>The combination of the different geophysical data available with the geological data should improve our tectonic modelling.</p>

DRFS: Detecting Risk Factor of Stroke Disease from Social Media Using Machine Learning Techniques

2020 ◽  
Author(s):  
S. Pradeepa ◽  
K. R. Manjula ◽  
S. Vimal ◽  
Mohammad S. Khan ◽  
Naveen Chilamkurti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Risk Factor ◽  
Machine Learning Techniques ◽  
Learning Techniques

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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