A Duration-Dependent Regime Switching Model for an Open Emerging Economy

We employ duration-dependent Markov-switching vector auto-regression (DDMSVAR) methodology to construct an economic cycle model for an emerging economy. By modifying the software codes for DDMSVAR methodology written by Pelagatti (2003), we show how to estimate the economic cycles in an emerging economy where macroeconomic shocks are suddenly observed and their levels are deep. The monthly values of net international reserves, domestic debt, inflation and industrial production in the Turkish economy from January 1989 to July 2007 are used for constructing the empirical analysis. Empirical evidence shows that DDMSVAR model can be successfully used in an emerging economy to estimate the cycles using basic macroeconomic indicators.duration dependent regime switching model, economic cycles, Markov models, Turkish economy

Predicción de crisis financiera de empresas

Financial crises are one of the most common phenomena in the economy.  This research studies important variables in predicting the financial crisis and bankruptcy of companies and have identified the most important financial variables in predicting the financial crisis. After identification, the most important predictors of bankruptcy and a model for forecasting the financial crisis and bankruptcy of the companies have been presented and its predictive power has been tested. To identify the most important variables in the prediction of the financial crisis and bankruptcy of companies, a linear separation function model has been used and a 9-variable model has been designed and presented. The results of the survey show that up to five years before the financial crisis can be predicted using a relatively high accuracy model.Las crisis financieras son uno de los fenómenos más comunes en la economía. Esta investigación estudia variables importantes para predecir la crisis financiera y la bancarrota de las empresas y ha identificado las variables financieras más importantes para predecir la crisis financiera. Después de la identificación, se presentaron los factores predictivos más importantes de la bancarrota y un modelo para pronosticar la crisis financiera y la bancarrota de las empresas y se probó su poder predictivo. Para identificar las variables más importantes en la predicción de crisis financiera y quiebra de empresas, en este estudio se ha utilizado un modelo de función de separación lineal y se ha diseñado y presentado un modelo de 9 variables. Los resultados de la encuesta muestran que se pueden predecir hasta cinco años antes de la crisis financiera mediante un modelo de precisión relativamente alta

Comprehensive Study of Automatic Speech Emotion Recognition Systems

Speech emotion recognition (SER) is the technology that recognizes psychological characteristics and feelings from the speech signals through techniques and methodologies. SER is challenging because of more considerable variations in different languages arousal and valence levels. Various technical developments in artificial intelligence and signal processing methods have encouraged and made it possible to interpret emotions.SER plays a vital role in remote communication. This paper offers a recent survey of SER using machine learning (ML) and deep learning (DL)-based techniques. It focuses on the various feature representation and classification techniques used for SER. Further, it describes details about databases and evaluation metrics used for speech emotion recognition

Hybrid Neural Networks for Learning the Trend in Time Series

The trend of time series characterizes the intermediate upward and downward behaviour of time series. Learning and forecasting the trend in time series data play an important role in many real applications, ranging from resource allocation in data centers, load schedule in smart grid, and so on. Inspired by the recent successes of neural networks, in this paper we propose TreNet, a novel end-to-end hybrid neural network to learn local and global contextual features for predicting the trend of time series. TreNet leverages convolutional neural networks (CNNs) to extract salient features from local raw data of time series. Meanwhile, considering the long-range dependency existing in the sequence of historical trends of time series, TreNet uses a long-short term memory recurrent neural network (LSTM) to capture such dependency. Then, a feature fusion layer is to learn joint representation for predicting the trend. TreNet demonstrates its effectiveness by outperforming CNN, LSTM, the cascade of CNN and LSTM, Hidden Markov Model based method and various kernel based baselines on real datasets

Condition-based maintenance—an extensive literature review

This paper presents an extensive literature review on the field of condition-based maintenance (CBM). The paper encompasses over 4000 contributions, analysed through bibliometric indicators and meta-analysis techniques. The review adopts Factor Analysis as a dimensionality reduction, concerning the metric of the co-citations of the papers. Four main research areas have been identified, able to delineate the research field synthetically, from theoretical foundations of CBM; (i) towards more specific implementation strategies (ii) and then specifically focusing on operational aspects related to (iii) inspection and replacement and (iv) prognosis. The data-driven bibliometric results have been combined with an interpretative research to extract both core and detailed concepts related to CBM. This combined analysis allows a critical reflection on the field and the extraction of potential future research directions

Development and applications of neural networks for economic forecasting

Neural networks are one of a variety of machine learning models which are beginning to be widely used in economic forecasting applications. Despite this, there is relatively little understanding of the conditions in which neural networks provide accurate forecasts, the uncertainty bounds which can be put on such forecasts, and the most suitable network types and parameters for forecasting in the relatively small-sample settings encountered within economics. This thesis fits into a growing body of literature which aims to answer some of these questions. In Chapter 1, we present a detailed study of the accuracy of neural networks for forecasting financial volatility, and present a novel adaptation of networks for time series data. In Chapter 2, we present an adaptation to the output layer of neural networks which allows the generation of prediction intervals for forecasts, and present variants to the architecture which improve the accuracy of these prediction intervals. In Chapter 3, we focus on confidence intervals, and present the first simulation study of the suitability of bootstrapping for neural networks for generating confidence intervals with correct coverage. Finally, in Chapter 4, we focus on an alternate application of neural networks in forecasting; that of converting free-form text data into indices, and present a novel neural network architecture for improving the recognition of named entities (companies, people etc.) in text, a necessary first step in such forecasting

Prosody in text-to-speech synthesis using fuzzy logic

For over a thousand years, inventors, scientists and researchers have tried to reproduce human speech. Today, the quality of synthesized speech is not equivalent to the quality of real speech. Most research on speech synthesis focuses on improving the quality of the speech produced by Text-to-Speech (TTS) systems. The best TTS systems use unit selection-based concatenation to synthesize speech. However, this method is very timely and the speech database is very large. Diphone concatenated synthesized speech requires less memory, but sounds robotic. This thesis explores the use of fuzzy logic to make diphone concatenated speech sound more natural. A TTS is built using both neural networks and fuzzy logic. Text is converted into phonemes using neural networks. Fuzzy logic is used to control the fundamental frequency for three types of sentences. In conclusion, the fuzzy system produces f0 contours that make the diphone concatenated speech sound more natural