Stock transaction prediction modeling and analysis based on LSTM

Siyuan Liu · Guangzhong Liao · Yifan Ding

96 citations0/0 files storedIEEE Conference on Industrial Electronics and Applications (ICIEA)

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Stock Market Forecasting Methods Hydrological Forecasting Using AI Time Series Analysis and Forecasting

Abstract

Stock price volatility is a highly complex nonlinear dynamic system. The stock's trading volume affects the stock's self correlation, self correlation and inertial effect, and the adjustment of the stock is not to advance with a homogeneous time process, which has its own independent time to promote the process. LSTM (Term Memory Long-Short) is a kind of time recurrent neural network, which is suitable for processing and predicting the important events of interval and long delay in time series. Based on temporal characteristics of stock and LSTM neural network algorithm, this paper uses the LSTM recurrent neural networks to filter, extract feature value and analyze the stock data, and set up the the prediction model of the corresponding stock transaction.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 96 citations exist upstream.

References

2005Clustering of financial time series with application to index and enhanced index tracking portfolio

Cited by

2023Real-time forecasting of time series in financial markets using sequentially trained dual-LSTMs 2019A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning 2021Predicting LQ45 financial sector indices using RNN-LSTM 2023Stock Market Analysis and Prediction Using LSTM: A Case Study on Technology Stocks 2020Sentiment-Driven Price Prediction of the Bitcoin based on Statistical and Deep Learning Approaches 2020A Hybrid Prediction Method for Stock Price Using LSTM and Ensemble EMD 2021Research on a hybrid prediction model for stock price based on long short-term memory and variational mode decomposition 2024News-driven stock market index prediction based on trellis network and sentiment attention mechanism

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.