May 7, 2022 · The Recurrent Neural Network (RNN) is one of the promising ANNs that has shown accurate results for time series forecasting. It is made up of a ...
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Apr 11, 2023 · In this paper, we propose to harness the power of CNNs and Transformers to model both short-term and long-term dependencies within a time series.
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May 11, 2022 · This paper aims to forecast: (i) the closing price of eight stock market indexes; and (ii) the closing price of six currency exchange rates related to the USD.
In this chapter, we will describe the basics of traditional time series analyses, discuss how neural net- works work, show how to implement time series ...
Mar 5, 2024 · In this beginner's guide, we'll explore the advantages and limitations of LSTM, learn how to prepare financial data, and discover the steps to build, train, ...
Neural-Net-with-Financial-Time-Series-Data is an open source software project using endogenous factors to predict daily log return of financial asset.
Feb 20, 2023 · In this paper, we propose a new financial time series forecasting model based on the deep learning ensemble model.
May 7, 2022 · This paper aims to forecast: (i) the closing price of eight stock market indexes; and (ii) the closing price of six currency exchange rates ...
This paper contains a financial forecast using Artificial Neural Networks. The analysis uses the traditional Backpropagation algorithm, followed by Resilient ...
In this paper, we propose a multi-modality graph neural network (MAGNN) to learn from these multimodal inputs for financial time series prediction.
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