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Multi-Scale Event Detection in Financial Time Series
Information published in the communication media, such as government transitions, economic crises, or corruption scandals, is an external factor...
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Ensemble of temporal Transformers for financial time series
The accuracy of price forecasts is important for financial market trading strategies and portfolio management. Compared to traditional models such as...
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Lagging problem in financial time series forecasting
Accurate financial time series forecasting is important in financial markets. However, for financial time series with low fluctuation, there is an...
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Directional Prediction of Financial Time Series Using SVM and Wilson Loop Perceptron
The Wilson loop is indicative of the pathway encompassed within the market cocycle, which carries the coherent gauge field behavior present in the...
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Short-term Kullback–Leibler divergence analysis to extract unstable periods in financial time series
A new method is presented for estimating a short-term Kullback-Leibler divergence to analyze the statistical characteristics of significant...
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Detection of Uncertainty Events in the Brazilian Economic and Financial Time Series
Economic policy uncertainty shocks change how the economy behaves, moving it away from its pattern. Therefore, these effects can be understood as an...
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Towards an efficient machine learning model for financial time series forecasting
Financial time series forecasting is a challenging problem owing to the high degree of randomness and absence of residuals in time series data....
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Exploring Long-Memory Process in the Prediction of Interval-Valued Financial Time Series and Its Application
Long-memory process has been widely studied in classical financial time series analysis, which has merely been reported in the field of...
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HXPY: A High-Performance Data Processing Package for Financial Time-Series Data
A tremendous amount of data has been generated by global financial markets everyday, and such time-series data needs to be analyzed in real time to...
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Financial time series
There are several characteristics different financial assets share that have been identified through empirical observations over time. These are...
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Fuzzy clustering of financial time series based on volatility spillovers
In this paper we propose a framework for fuzzy clustering of time series based on directional volatility spillovers. In the case of financial time...
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Clustering Financial Time Series by Dependency
In this paper, we propose a procedure for clustering financial time series by dependency on their volatilities. Our procedure is based on the...
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Tail dependence-based fuzzy clustering of financial time series
In this paper, we propose a new fuzzy clustering of time series with entropy regularization. Following a model-based approach, the dissimilarity...
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Predicting the state of synchronization of financial time series using cross recurrence plots
Cross-correlation analysis is a powerful tool for understanding the mutual dynamics of time series. This study introduces a new method for predicting...
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Financial time series prediction under Covid-19 pandemic crisis with Long Short-Term Memory (LSTM) network
In this paper, we design and apply the Long Short-Term Memory (LSTM) neural network approach to predict several financial classes’ time series under...
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Financial time series forecasting based on momentum-driven graph signal processing
Forecasting is important for social development and industrial production in today’s complex and fluctuating economic environment. The nonlinearity...
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A novel deep transfer learning framework with adversarial domain adaptation: application to financial time-series forecasting
Financial market prediction is generally regarded as one of the most challenging tasks in data mining. Recent deep learning models have achieved...
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Forecasting financial time series with Boltzmann entropy through neural networks
Neural networks have recently been established as state-of-the-art in forecasting financial time series. However, many studies show how one...
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De-noising classification method for financial time series based on ICEEMDAN and wavelet threshold, and its application
This paper proposes a classification method for financial time series that addresses the significant issue of noise. The proposed method combines...
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An Efficient GAN-Based Multi-classification Approach for Financial Time Series Volatility Trend Prediction
Deep learning has achieved tremendous success in various applications owing to its robust feature representations of complex high-dimensional...
