Aiming at the problem of stock trend prediction, we present a hybrid model based on Long Short-Term Memory (LSTM) and Auto Regressive Integrated Moving Average (ARIMA) approach to predict stock prices. Through sample training and experiments in multiple data sets and different market stages, it ...
Finally, another advanced model used to predict stock prices and trends isthe long short-term memory (LSTM) model.This model is similar to the ESN model in the sense thatit can predict stock prices with high accuracy.The model works by capturing historical trend patterns to predict future valu...
One of the essential steps is an evaluation of the expected return, which is necessary for the calculation of an event’s impact on stock prices. The time series is handled using the TFT model. To get a sense of its performance within our problem setting, we estimate the mean value of ...
Using deep unsupervised learning (Self-organized Maps) we will try to spot anomalies in every day's pricing. Anomaly (such as a drastic change in pricing) might indicate an event that might be useful for the LSTM to learn the overall stock pattern. Next, having so many features, we need...
Contextual evidence from the MEEMD-LSTM-MLP approach 2024, North American Journal of Economics and Finance Citation Excerpt : By leveraging the advantages of both components, it offers a comprehensive analysis of the complex dynamics of the stock market, thereby significantly enhancing the predictive ...
Using deep unsupervised learning (Self-organized Maps) we will try to spot anomalies in every day's pricing. Anomaly (such as a drastic change in pricing) might indicate an event that might be useful for the LSTM to learn the overall stock pattern. Next, having so many features, we need...
Hands-on Time Series Anomaly Detection using Autoencoders, with Python Data Science Here’s how to use Autoencoders to detect signals with anomalies in a few lines of… Piero Paialunga August 21, 2024 12 min read Feature engineering, structuring unstructured data, and lead scorin...
Using these transforms we will eliminate a lot of noise (random walks) and create approximations of the real stock movement. Having trend approximations can help the LSTM network pick its prediction trends more accurately. Autoregressive Integrated Moving Average (ARIMA) - This was one of the most...
Using deep unsupervised learning (Self-organized Maps) we will try to spot anomalies in every day's pricing. Anomaly (such as a drastic change in pricing) might indicate an event that might be useful for the LSTM to learn the overall stock pattern. Next, having so many features, we need...
Predicting the direction of stock market prices using tree-based classifiers N Am J Econ Financ (2019) E. Silva et al. A neural network based approach to support the Market Making strategies in High-Frequency Trading Hossein Hassani et al. "Big-Crypto: Big data, blockchain and cryptocurrency...