We propose a robust framework consisting of a suite of deep learning-based regression models that yields a very high level of accuracy in forecasting of stock prices. The models are built using the historical stock price data of a well-known company listed in the National Stock Exchange (NSE)...
Title:Curriculum Learning in Deep Neural Networks for Financial Forecasting Authors:Allison Koenecke, [Amita Gajewar 对于任何金融组织来说,计算各种产品的准确季度预测都是最关键的操作之一。随着需要预测的粒度增加,传统的统计时间序列模型可能无法很好地扩展。我们尝试使用自然语言处理(编码器-解码器 LSTM)和计算机...
timeseriesforecasting,CNN,LSTM,RNN1.IntroductionThefinanceindustryhasalwaysbeeninterestedinsuccessfulpredictionoffinancialtimeseriesdata.NumerousstudieshavebeenpublishedthatwerebasedonMLmodelswithrelativelybetterperformancescomparedtoclassicaltimeseriesforecastingtechniques.Meanwhile,thewidespreadapplicationofautomated...
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 ...
A comparative analysis of forecasting financial time series using arima, lstm, and bilstm[J]. arXiv preprint arXiv:1911.09512, 2019 Google Scholar 15 Eapen J, Bein D, Verma A. Novel deep learning model with CNN and bi-directional LSTM for improved stock market index prediction[C]//2019 ...
Financial time series data are characterized by non-linearity, non-stationarity, and stochastic complexity, so predicting such data presents a significant challenge. This paper proposes a novel hybrid model for financial forecasting based on CEEMDAN-SE and ARIMA- CNN-LSTM. With the help of the CEEM...
We aim to use large language models (LLMs) for multi-features time series forecasting in stock prediction, where we leverage multiple alphas (domainspecific time series features) and their explanations (text features) to frame the task as next-token prediction. However, existing methods are often...
For the first time, the study by Yang et al. (2020) proposes a framework for how humans can be integrated into the forecasting process when the prediction is conducted using AI. In their study, the forecasting process is considered as a cycle in which humans are involved, from exploring ...
(CNN–LSTM), which incorporate historical log-return series and time-series data in an image format to predict the volatility of gold spot prices. Likewise, various studies have used deep-learning models for crude oil price forecasting. Orojo et al. (2019) employed a multirecurrent network to...
Selvin S, Vinayakumar R, Gopalakrishnan EA, Menon VK, Soman KP (2017) Stock price prediction using LSTM, RNN and CNN-sliding window model, ICACCI, Udupi, India, pp 1643–1647 Sezer OB, Gudelek MU, Ozbayoglu AM (2020) Financial time series forecasting with deep learning: a systematic lit...