Long short-term memory networks for earthquake detection in Venezuelan regions

Reliable earthquake detection and location algorithms are necessary to properly catalog and analyze the continuously growing seismic records. This paper reports the results of applying Long Short-Term Memory (LSTM) networks to single-station three-channel waveforms for P-wave earthquake detection in western and north central regions of Venezuela. Precisely, we apply our technique to study the seismicity along the dextral strike-slip Boconó and La Victoria - San Sebastián faults, with complex tectonics driven by the interactions between the South American and Caribbean plates.Peer ReviewedPostprint (author's final draft

Financial crises and bank failures: a review of prediction methods

In this article we analyze financial and economic circumstances associated with the U.S. subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. We suggest that the level of cross-border holdings of long-term securities between the United States and the rest of the world may indicate a direct link between the turmoil in the securitized market originated in the United States and that in other countries. We provide a summary of empirical results obtained in several Economics and Operations Research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults; we also extensively outline the methodologies used in them. The intent of this article is to promote future empirical research for preventing financial crises.Subprime mortgage ; Financial crises

Financial crises and bank failures: a review of prediction methods

In this article we provide a summary of empirical results obtained in several economics and operations research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults, as well as outlines of the methodologies used. We analyze financial and economic circumstances associated with the US subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. The intent of the article is to promote future empirical research that might help to prevent bank failures and financial crises.financial crises; banking failures; operations research; early warning methods; leading indicators; subprime markets

A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons

With the globally increasing electricity demand, its related uncertainties are on the rise as well. Therefore, a deeper insight of load forecasting techniques for projecting future electricity demands becomes imperative for business entities and policy makers. The electricity demand is governed by a set of different variables or “electricity demand determinants”. These demand determinants depend on forecasting horizons (long term, medium term, and short term), the load aggregation level, climate, and socio-economic activities. In this paper, a review of different electricity demand forecasting methodologies is provided in the context of a group of low and middle income countries. The article presents a comprehensive literature review by tabulating the different demand determinants used in different countries and forecasting the trends and techniques used in these countries. A comparative review of these forecasting methodologies over different time horizons reveals that the time series modeling approach has been extensively used while forecasting for long and medium terms. For short term forecasts, artificial intelligence-based techniques remain prevalent in the literature. Furthermore, a comparative analysis of the demand determinants in these countries indicates a frequent use of determinants like the population, GDP, weather, and load data over different time horizons. Following the analysis, potential research gaps are identified, and recommendations are provided, accordingly

We need to go deeper: measuring electoral violence using convolutional neural networks and social media

Electoral violence is conceived of as violence that occurs contemporaneously with elections, and as violence that would not have occurred in the absence of an election. While measuring the temporal aspect of this phenomenon is straightforward, measuring whether occurrences of violence are truly related to elections is more difficult. Using machine learning, we measure electoral violence across three elections using disaggregated reporting in social media. We demonstrate that our methodology is more than 30 percent more accurate in measuring electoral violence than previously utilized models. Additionally, we show that our measures of electoral violence conform to theoretical expectations of this conflict more so than those that exist in event datasets commonly utilized to measure electoral violence including ACLED, ICEWS, and SCAD. Finally, we demonstrate the validity of our data by developing a qualitative coding ontology

Credit Pit Detection in Subordinate Securities: A French Perspective

The purpose of this research is to prepare a predictive model for identifying credit crisis using an artificial neural network. The paper also aims to find out the driver and driven relationship between various financial instruments like CDS, FRA, IRS, and the Volatility index (VCAC) and government securities for France. The model, thus, is directed towards finding a threshold for credit pit events and linking various events corresponding to that dates where the threshold is breached to validate the accuracy and usefulness of the model. From the research, it is found that for France, the CDS-FRA-VCAC model derives the threshold for VCAC to indicate the probability of credit crisis or financial market crash. It is also found that sovereign bonds have a huge impact on France economy including various derivatives. This is probably why the Eurozone debt crisis impacted France much more than the 2008 financial crash