Modelling of Hyperparameter Tuned Bidirectional Long Short-Term Memory with TLBO for Stock Price Prediction Model
Keywords:
Stock price prediction, stock market, time series, prediction, deep learning, hyperparameter tuning, , TLBO algorithmAbstract
Because of the stock market's importance as a forum for investors, precise forecasting of stock market developments remains a popular study topic among monetary industry executives and academics. Forecasting stock prices is an exciting endeavour that is complicated by the stock Earlier research that used accurate replicas and machine learning approaches to forecast short-term changes in stock prices was primarily focused on forecasting short-term changes in stock prices. Considering this, research develops an Hyperparameter Tuned Bidirectional Long Short-Term Memory (HPT-BiLSTM), a unique BiLSTM for stock price estimationThe recommended HPT-HCLSTM method combines forecasting, parameter optimization, and preprocessing. The HPT-BiLSTM approach is used in the HPT-BiLSTM strategy to predict stock prices. The BiLSTM technique's hyper parameters are also improved using the teaching and knowledge-based optimization (TLBO) strategy, leading to noticeably decreased error levels. A variety of imitations were carried out in order to confirm the HPT-BiLSTM method's enhanced forecast presentation, and the significances established that the technique's higher presentation could be noticed in a number of different facets.
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