scholarly journals GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies

2021 ◽  
Vol 14 (9) ◽  
pp. 421
Author(s):  
Fahad Mostafa ◽  
Pritam Saha ◽  
Mohammad Rafiqul Islam ◽  
Nguyet Nguyen
Keyword(s):  
Value At Risk ◽  
Garch Model ◽  
Graphical Analysis ◽  
Learning Approaches ◽  
Market Prices ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
The Garch Model ◽  
Mean Square Errors ◽  
Better Than

Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way. This study implements some statistical and machine learning approaches for cryptocurrency investments. First, we implement GJR-GARCH over the GARCH model to estimate the volatility of ten popular cryptocurrencies based on market capitalization: Bitcoin, Bitcoin Cash, Bitcoin SV, Chainlink, EOS, Ethereum, Litecoin, TETHER, Tezos, and XRP. Then, we use Monte Carlo simulations to generate the conditional variance of the cryptocurrencies using the GJR-GARCH model, and calculate the value at risk (VaR) of the simulations. We also estimate the tail-risk using VaR backtesting. Finally, we use an artificial neural network (ANN) for predicting the prices of the ten cryptocurrencies. The graphical analysis and mean square errors (MSEs) from the ANN models confirmed that the predicted prices are close to the market prices. For some cryptocurrencies, the ANN models perform better than traditional ARIMA models.

scholarly journals Using the artificial neural networks for prediction and validating solar radiation

2019 ◽  
Vol 27 (1) ◽  
Author(s):  
Zahraa E. Mohamed
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Horizontal Surface ◽  
The Other ◽  
Rate Coefficients ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Statistical Indicators ◽  
Better Than

AbstractThe main objective of this paper is to employ the artificial neural network (ANN) models for validating and predicting global solar radiation (GSR) on a horizontal surface of three Egyptian cities. The feedforward backpropagation ANNs are utilized based on two algorithms which are the basic backpropagation (Bp) and the Bp with momentum and learning rate coefficients respectively. The statistical indicators are used to investigate the performance of ANN models. According to these indicators, the results of the second algorithm are better than the other. Also, model (6) in this method has the lowest RMSE values for all cities in this study. The study indicated that the second method is the most suitable for predicting GSR on a horizontal surface of all cities in this work. Moreover, ANN-based model is an efficient method which has higher precision.

scholarly journals Different ANN Models for Short Term Electricity Price Forecasting

2019 ◽  
Vol 8 (3) ◽  
pp. 6706-6712
Keyword(s):  
Neural Network ◽  
Market Price ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Price Forecasting ◽  
Market Prices ◽  
Electricity Price Forecasting ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Pool Market

In a deregulated electiricity market, price forecasting is gaining demand with application of Artificial Neural Network (ANN). The paper deals with price forecasting with different ANN models.like Back Propagation Neural Network( BPNN), Radial Bias Function Neural Network (RBFNN) and Genectic Algorithm based Neural Network (GANN). A contextual investigation is made with the downloaded data of the day-ahead pool market prices of the California Pool Market using the above four different ANN models and the results are compared.

scholarly journals Fast Detection and Traceback-based Mitigation of Interest Flooding Attack

2021 ◽  
Author(s):  
Naveen Kumar ◽  
Shashank Srivast
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Learning Approaches ◽  
Previous Approach ◽  
State Information ◽  
Fast Detection ◽  
Flooding Attack ◽  
Artificial Neural Network Ann ◽  
Ann Classifier ◽  
Better Than

Abstract NDN Pending Interest Table (PIT) helps NDN by storing the state of a request within the router. This state information helps the router to redirect the data packet towards the requester. However, an attacker can send malicious requests, which could flood the PIT; this attack is known as Interest Flooding Attack (IFA). In our previous work, we have found the most relevant features needed to detect IFA and applied a few machine learning approaches for the offline detection of IFA. In this article, a trained Artificial Neural Network (ANN) classifier has been deployed on each NDN router for the online detection of IFA. A novel traceback-based mitigation is proposed, which is triggered after the detection. The proposed approach is found better than the previous approach in terms of the satisfaction ratio and throughput of the legitimate consumers.

Dental Data Mining: Potential Pitfalls and Practical Issues

2003 ◽  
Vol 17 (1) ◽  
pp. 109-114 ◽  
Author(s):  
S.A. Gansky
Keyword(s):  
Data Mining ◽  
Operating Characteristic ◽  
Regression Tree ◽  
Careful Analysis ◽  
Classification And Regression Tree ◽  
Receiver Operating Characteristic Curves ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Sensitivity Specificity ◽  
Better Than

Knowledge Discovery and Data Mining (KDD) have become popular buzzwords. But what exactly is data mining? What are its strengths and limitations? Classic regression, artificial neural network (ANN), and classification and regression tree (CART) models are common KDD tools. Some recent reports ( e.g., Kattan et al., 1998 ) show that ANN and CART models can perform better than classic regression models: CART models excel at covariate interactions, while ANN models excel at nonlinear covariates. Model prediction performance is examined with the use of validation procedures and evaluating concordance, sensitivity, specificity, and likelihood ratio. To aid interpretation, various plots of predicted probabilities are utilized, such as lift charts, receiver operating characteristic curves, and cumulative captured-response plots. A dental caries study is used as an illustrative example. This paper compares the performance of logistic regression with KDD methods of CART and ANN in analyzing data from the Rochester caries study. With careful analysis, such as validation with sufficient sample size and the use of proper competitors, problems of naïve KDD analyses ( Schwarzer et al., 2000 ) can be carefully avoided.

scholarly journals Jump Aggregation, Volatility Prediction, and Nonlinear Estimation of Banks’ Sustainability Risk

2020 ◽  
Vol 12 (21) ◽  
pp. 8849
Author(s):  
Zhouwei Wang ◽  
Qicheng Zhao ◽  
Min Zhu ◽  
Tao Pang
Keyword(s):  
Quantile Regression ◽  
Stock Prices ◽  
Value At Risk ◽  
Stock Price ◽  
Garch Model ◽  
Jump Diffusion ◽  
Support Vector ◽  
Garch Models ◽  
Bank Stock ◽  
Better Than

Extreme financial events usually lead to sharp jumps in stock prices and volatilities. In addition, jump clustering and stock price correlations contribute to the risk amplification acceleration mechanism during the crisis. In this paper, four Jump-GARCH models are used to forecast the jump diffusion volatility, which is used as the risk factor. The linear and asymmetric nonlinear effects are considered, and the value at risk of banks is estimated by support vector quantile regression. There are three main findings. First, in terms of the volatility process of bank stock price, the Jump Diffusion GARCH model is better than the Continuous Diffusion GARCH model, and the discrete jump volatility is significant. Secondly, due to the difference of the sensitivity of abnormal information shock, the jump behavior of bank stock price is heterogeneous. Moreover, CJ-GARCH models are suitable for most banks, while ARJI-R2-GARCH models are more suitable for small and medium sized banks. Thirdly, based on the jump diffusion volatility information, the performance of the support vector quantile regression is better than that of the parametric quantile regression and nonparametric quantile regression.

scholarly journals A comparison of artificial neural networks used for river forecasting

1999 ◽  
Vol 3 (4) ◽  
pp. 529-540 ◽  
Author(s):  
C. W. Dawson ◽  
R. L. Wilby
Keyword(s):  
River Flow ◽  
Radial Basis Function Network ◽  
Multiple Linear Regression Model ◽  
Rainfall Runoff ◽  
Diagnostic Statistics ◽  
Ann Models ◽  
Validation Technique ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Better Than

Abstract. This paper compares the performance of two artificial neural network (ANN) models – the multi layer perceptron (MLP) and the radial basis function network (RBF) – with a stepwise multiple linear regression model (SWMLR) and zero order forecasts (ZOF) of river flow. All models were trained using 15 minute rainfall-runoff data for the River Mole, a flood-prone tributary of the River Thames, UK. The models were then used to forecast river flows with a 6 hour lead time and 15 minute resolution, given only antecedent rainfall and discharge measurements. Two seasons (winter and spring) were selected for model testing using a cross-validation technique and a range of diagnostic statistics. Overall, the MLP was more skillful than the RBF, SWMLR and ZOF models. However, the RBF flow forecasts were only marginally better than those of the simpler SWMLR and ZOF models. The results compare favourably with a review of previous studies and further endorse claims that ANNs are well suited to rainfall-runoff modelling and (potentially) real-time flood forecasting.

scholarly journals Estimating Inside Air Temperature of a Glasshouse Using Statistical Models

2017 ◽  
Vol 12 (1) ◽  
pp. 01-05 ◽  
Author(s):  
Aristidis Matsoukis ◽  
Konstantinos Chronopoulos
Keyword(s):  
Air Temperature ◽  
Mean Absolute Error ◽  
Predictor Variable ◽  
Absolute Error ◽  
Meteorological Station ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Better Than

The efficiency of applying linear regression (LR) and artificial neural network (ANN) models to estimate inside air temperature (T) of a glasshouse (37o48΄20΄΄N, 23o57΄48΄΄E), Lavreotiki, was investigated in the present work. The T data from an urban meteorological station (MS) at 37058΄55΄΄N, 23o32΄14΄΄E, Athens, Attica, Greece, about 30 Km away from the glasshouse, were used as predictor variable, taking into account the actual time of measurement (ATM) and two hours earlier (ATM-2), depending on the case. Air temperature data were monitored in each examined area (glasshouse and MS) for four successive months (July-October) and averages on a two-hour basis were used for the aforementioned estimation. Results showed that ANN were better than LR models, considering their better performance as shown in the scatterplots of the distribution of observed versus estimated inside T data of the glasshouse, in terms of both higher coefficient of determination (R2) and lower mean absolute error (MAE). The best ANN model (highest R2 and lowest MAE) was achieved by using as predictor variables the T at ATM and the T at ATM-2 from MS. The findings of our study may be a first step towards the estimation of inside T of a glasshouse in Greece, from outside T data of a remote MS. Thus, the operation of the glasshouse could be improved noticeably.

scholarly journals Deep neural networks detect suicide risk from textual facebook posts

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yaakov Ophir ◽  
Refael Tikochinski ◽  
Christa S. C. Asterhan ◽  
Itay Sisso ◽  
Roi Reichart
Keyword(s):  
Social Media ◽  
Suicide Risk ◽  
Text Representation ◽  
Psychosocial Risks ◽  
Task Model ◽  
Ann Models ◽  
Text Features ◽  
Artificial Neural Network Ann ◽  
Complicated Task ◽  
Better Than

Abstract Detection of suicide risk is a highly prioritized, yet complicated task. Five decades of research have produced predictions slightly better than chance (AUCs = 0.56–0.58). In this study, Artificial Neural Network (ANN) models were constructed to predict suicide risk from everyday language of social media users. The dataset included 83,292 postings authored by 1002 authenticated Facebook users, alongside valid psychosocial information about the users. Using Deep Contextualized Word Embeddings for text representation, two models were constructed: A Single Task Model (STM), to predict suicide risk from Facebook postings directly (Facebook texts → suicide) and a Multi-Task Model (MTM), which included hierarchical, multilayered sets of theory-driven risk factors (Facebook texts → personality traits → psychosocial risks → psychiatric disorders → suicide). Compared with the STM predictions (0.621 ≤ AUC ≤ 0.629), the MTM produced significantly improved prediction accuracy (0.697 ≤ AUC ≤ 0.746), with substantially larger effect sizes (0.729 ≤ d ≤ 0.936). Subsequent content analyses suggested that predictions did not rely on explicit suicide-related themes, but on a range of text features. The findings suggest that machine learning based analyses of everyday social media activity can improve suicide risk predictions and contribute to the development of practical detection tools.

Sign in / Sign up

Export Citation Format

Share Document