Analysis and Forecasting of Financial Time Series Using CNN and LSTM-Based Deep Learning ModelsThis book presents recent advances in the field of scalable distributed computing including state-of-the-art researc
Financial time series prediction is a subset of time series analysis, which in general is a challenging task. In particular, financial time series prediction involves forecasting future values of financial indicators, such as stock prices, exchange rates, and commodity prices, based on historical data...
Deep Learning provided powerful tools for forecasting financial time series data. However, despite the success of these approaches on many challenging financial forecasting tasks, it is not always straightforward to employ DL-based approaches for highly volatile and non-stationary time financial series. ...
Using the daily closing prices of the Shanghai stock index from October 8, 1996 to December 31, 2004, the authors find that neural networks can improve the forecasting quality. LSTM networks can detect correlation in nonlinear time series (such as financial time series) and produce predictions ...
models that are used, such as CNN, LSTM, Deep Reinforcement Learning (DRL). Sec- tion 4 will focus on the various financial time series forecasting implementation areas using DL, namely stock forecasting, index forecasting, trend forecasting, commodity forecasting, volatility forecasting, foreign ...
Therefore, we encourage to adopt the proposed method to the practitioners and provide a new thought, considering the analysis of cross-regional features, in the financial time-series forecasting. Introduction Due to the volatile, nonlinear, complicated, and chaotic characteristics of the financial ...
We present a novel approach for analyzing financial time series data using a Long Short-Term Memory Autoencoder (LSTMAE), a deep learning method. Our primary objective is to uncover intricate relationships among different stock indices, leading to the ex
Financial forecasting using ANFIS networks with Quantum-behaved Particle Swarm Optimization In this paper, we present a new hybrid intelligent method to forecast financial time series, especially for the Foreign Exchange Market (FX). To emulate... A Bagheri,HM Peyhani,M Akbari - 《Expert Systems ...
Abstract This paper presents a new forecasting approach using the Pelican Optimized Extreme Learning Machine (PO-ELM) model, designed to enhance the prediction of future trends based on historical data. The PO-ELM model refines the ELM by introducing the pelican optimizer, which systematically identi...
In: Symposium series on computational intelligence. Honolulu, Hawaï. 1–8 https://doi.org/10.1109/SSCI.2017.8280883 Livieris IE, Pintelas E, Pintelas P (2020) A CNN–LSTM model for gold price time-series forecasting. Neural Comput Appl 32(5):17351–17360 Article Google Scholar ...