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Application of empirical wavelet transform, particle swarm optimization, gravitational search algorithm and long short-term memory neural network to copper price forecasting
Copper is one of the main non-ferrous metals which are closely associated with important industries, such as equipment manufacturing, electrical...
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Portfolio Optimization with Prediction-Based Return Using Long Short-Term Memory Neural Networks: Testing on Upward and Downward European Markets
In recent years, artificial intelligence has helped to improve processes and performance in many different areas: in the field of portfolio...
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Enhancing Financial Risk Prediction Using TG-LSTM Model: An Innovative Approach with Applications to Public Health Emergencies
Amidst the backdrop of economic globalization and occasional public health crises, the comprehension and mitigation of financial risks confronting...
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LSTM–GARCH Hybrid Model for the Prediction of Volatility in Cryptocurrency Portfolios
In the present work, the volatility of the leading cryptocurrencies is predicted through generalised autoregressive conditional heteroskedasticity...
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Advanced Machine Learning for Financial Markets: A PCA-GRU-LSTM Approach
This study pioneers the integration of environmental data with financial indicators to forecast stock prices, employing a novel PCA-GRU-LSTM model....
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PCA-ICA-LSTM: A Hybrid Deep Learning Model Based on Dimension Reduction Methods to Predict S&P 500 Index Price
In this paper, we propose a new hybrid model based on a deep learning network to predict the prices of financial assets. The study addresses two key...
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Foreign Currency Exchange Rate Prediction Using Long Short-Term Memory, Support Vector Regression and Random Forest Regression
This chapter aims to predict the foreign currency exchange rate on the basis of the US dollar over twenty-two different currencies. This chapter...
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Prophet-LSTM-BP Ensemble Carbon Trading Price Prediction Model
Accurately identifying changes in carbon trading prices can provide reasonable reference indicators for a government's macrocontrol and can also help...
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Forecasting the Stock Market Index with Dynamic ARIMA Model and LSTM Model
With the development of the machine learning method, there are a lot more time series model being invented and applied to mimic the real-world data....
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Improving Sliding Window Effect of LSTM in Stock Prediction Based on Econometrics Theory
This study examines the influence of the sliding window in the LSTM model on its predictive performance in the stock market. The investigation...
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Unemployment rate forecasting: LSTM-GRU hybrid approach
Unemployment rates provide information on the economic development of countries. Unemployment is not only an economic problem but also a social one....
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Challenges of Stock Prediction Based on LSTM Neural Network
For a long time, many scholars and researchers have tried stock forecasting. Stock forecasting has always been the most concerned and challenged in...
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Enhancing Sustainable Development Through Sentiment Analysis of Public Digital Resources: A PSO-LSTM Approach
In recent years, the global paradigm of sustainable development has gained prominence, emphasizing the need to address present challenges while...
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Going a Step Deeper Down the Rabbit Hole: Deep Learning Model to Measure the Size of the Unregistered Economy Activity
Accurately estimating the size of unregistered economies is crucial for informed policymaking and economic analysis. However, many studies seem to...
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Predictability of short-term passengers’ origin and destination demands in urban rail transit
Accurate prediction of short-term passengers’ origin and destination (OD) demands is key to efficient operation and management of urban rail transit...
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Reference Vector-Based Multiobjective Clustering Ensemble Approach for Time Series Forecasting
This paper integrates the maximal overlap discrete wavelet transform (MODWT), long and short-term memory neural network (EA-LSTM) of evolutionary...
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Forecasting Stock Indices: Stochastic and Artificial Neural Network Models
In recent years, there has been a bloom in the stock investors due to availability of various platforms that have provided an opportunity even for...
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On Forecasting Realized Volatility for Bitcoin Based on Deep Learning PSO–GRU Model
As the trendsetter of the digital currency market, Bitcoin fluctuates dramatically in a short period of time and has received increasing attention...
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Predicting Cryptocurrency Price Returns by Using Deep Learning Model of Technical Analysis Indicators
Over the last few years, cryptocurrencies have become a new alternative exchange currency for the global economy. Due to the high volatility in the...
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Comparative analysis of deep-learning-based models for hourly bus passenger flow forecasting
An efficient transportation system is conducive to maintaining traffic flow and safety. Passenger flow forecasting (PFF), an area of traffic...
