Combining measures of signal complexity and machine learning for time series analyis: a review
S Raubitzek, T Neubauer - Entropy, 2021 - mdpi.com
Measures of signal complexity, such as the Hurst exponent, the fractal dimension, and the
Spectrum of Lyapunov exponents, are used in time series analysis to give estimates on …
Spectrum of Lyapunov exponents, are used in time series analysis to give estimates on …
An ensemble machine learning framework for Airbnb rental price modeling without using amenity-driven features
Purpose The prediction of Airbnb listing prices predominantly uses a set of amenity-driven
features. Choosing an appropriate set of features from thousands of available amenity …
features. Choosing an appropriate set of features from thousands of available amenity …
A differential evolution-based regression framework for forecasting Bitcoin price
This research proposes a differential evolution-based regression framework for forecasting
one day ahead price of Bitcoin. The maximal overlap discrete wavelet transformation first …
one day ahead price of Bitcoin. The maximal overlap discrete wavelet transformation first …
FEB-stacking and FEB-DNN models for stock trend prediction: a performance analysis for pre and post covid-19 periods
I Ghosh, TD Chaudhuri - Decision Making: Applications in …, 2021 - dmame-journal.org
In this paper, stock price prediction is perceived as a binary classification problem where the
goal is to predict whether an increase or decrease in closing prices is going to be observed …
goal is to predict whether an increase or decrease in closing prices is going to be observed …
Potentials and limitations of complexity research for environmental sciences and modern farming applications
K Mallinger, S Raubitzek, T Neubauer… - Current Opinion in …, 2024 - Elsevier
Open system analysis is prone to the oversimplification of dynamics due to tightly coupled
variables and their nonlinear, complex, and often unpredictable behavior. By assessing the …
variables and their nonlinear, complex, and often unpredictable behavior. By assessing the …
Co-movement and dynamic correlation of financial and energy markets: An integrated framework of nonlinear dynamics, wavelet analysis and DCC-GARCH
In this paper, we analyze the inherent evolutionary dynamics of financial and energy
markets. We study their inter-relationships and perform predictive analysis using an …
markets. We study their inter-relationships and perform predictive analysis using an …
The role of political risk, uncertainty, and crude oil in predicting stock markets: Evidence from the UAE economy
This study examines how the determinants of the political risk factor affect the forecasting
performance of the United Arab Emirates' stock market during the COVID-19 pandemic. The …
performance of the United Arab Emirates' stock market during the COVID-19 pandemic. The …
A granular deep learning approach for predicting energy consumption
This paper proposes a granular deep learning approach consisting of maximal overlap
discrete wavelet transformation (MODWT) and long short-term memory (LSTM) network for …
discrete wavelet transformation (MODWT) and long short-term memory (LSTM) network for …
Analysis of temporal pattern, causal interaction and predictive modeling of financial markets using nonlinear dynamics, econometric models and machine learning …
This paper presents a novel predictive modeling framework for forecasting the future returns
of financial markets. The task is very challenging as the movements of the financial markets …
of financial markets. The task is very challenging as the movements of the financial markets …
A granular machine learning framework for forecasting high-frequency financial market variables during the recent black swan event
This paper analyses highly voluminous 1-minute intraday movements of the closing prices of
Bitcoin, crude oil, the Dow Jones Industrial Average (DJIA) and the euro–US dollar …
Bitcoin, crude oil, the Dow Jones Industrial Average (DJIA) and the euro–US dollar …