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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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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...
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Mathematical Modeling of Financial Time Series Volatility: A GARCH Model
In the era of economic data modeling, machine learning algorithms, are increasingly suitable for big data, especially for univariate time series. The...
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Improving stock trend prediction through financial time series classification and temporal correlation analysis based on aligning change point
In order to improve the accuracy of stock prediction, people major in computer science and technology begin to apply their techniques to the...
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Construction of Financial Early Warning System Based on Binary Time Series Algorithm
It needs to establish a financial crisis early warning system that can be analyzed based on past data. Building a financial crisis early warning...
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A Comprehensive Survey on Higher Order Neural Networks and Evolutionary Optimization Learning Algorithms in Financial Time Series Forecasting
The financial market volatility has been a focus of study for experts over past decades. While stockbrokers and investors expect reliable projections...
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ARCH Model and Nonlinear Autoregressive Neural Networks for Forecasting Financial Time Series
A financial time series is most often based on representing financial prices (stock market indices). For this reason, the researchers focused on the...
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Construction Time Series Forecasting Using Multivariate Time Series Models
Identifying leading indicators of construction cost time series and using them as explanatory variables could improve the accuracy of forecasting...
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Integrated Prediction of Financial Time Series Data Based on Deep Learning
In the first test, we integrated the local correlation characteristics of financial market time series data and different models of sequence...
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An Effective GAN-Based Multi-classification Approach for Financial Time Series
Deep learning has achieved significant success in various applications due to its powerful feature representations of complex data. Financial time...
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Time Series Models
In this chapter we introduce the time series models in machine learning. These models are different in principle compared to most other models in the...
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Machine Learning for Real Estate Time Series Prediction
Several researchers have demonstrated that real estate investments have improved the risk-adjusted performance of mixed-asset portfolios belonging to...
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Signature-Based Community Detection for Time Series
Community detection for time series without prior knowledge poses an open challenge within complex networks theory. Traditional approaches begin by...
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ALAE: self-attention reconstruction network for multivariate time series anomaly identification
Multivariate time series from the real world has great application values, where its accurate anomaly identification has become an important research...
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Forecasting Financial Time Series Using Robust Deep Adaptive Input Normalization
Deep Learning provided powerful tools for forecasting financial time series data. However, despite the success of these approaches on many...
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Deep Learning Paradigm for Time Series Stock Prediction
Stocks have become established and widely recognised as a new electronic alternative exchange currency system, with significant consequences for...
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A Review on Topological Data Analysis in Time Series
In data science, a time series is a predefined method of examining a set of data points gathered over time. A considerable amount of data is required...
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Financial Time Series Forecasting Using Prophet
Forecasting the financial time series had been a difficult endeavor for both academia and businesses. Advances of the financial time series...
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Time series quantum classifiers with amplitude embedding
Quantum Machine Learning was born during the past decade as the intersection of Quantum Computing and Machine Learning. Today, advances in quantum...
