An improved LSTM technique using three-point moving gradient for stock price forecasting

• Main Authors: Wong, Pee Shoong, Asmai, Siti Azirah, Zulkarnain, Nur Zareen, Tay, Choo Chuan
• Format: Article
• Language: English
• Published: Machine Intelligence Research (MIR) Labs 2022
• Online Access: http://eprints.utem.edu.my/id/eprint/26447/2/2.3.1.2.2.%20AN%20IMPROVED%20LSTM%20TECHNIQUE%20USING%20THREE-POINT%20MOVING%20GRADIENT%20FOR%20STOCK%20PRICE%20FORECASTING.PDF; http://eprints.utem.edu.my/id/eprint/26447/; http://www.mirlabs.org/ijcisim/regular_papers_2022/IJCISIM_29.pdf

Everybody desires to see the future. The understanding of the time ahead can create enormous prospects, clout and fortune. For soothsayers who can predict the future financial developments, they are be able to command immense fortune. Therefore, predicting the financial stock market is always a great attraction with financiers and opportunists. Massive finance and equipment have been devoted into exploring the stock prices movements, hoping of realizing the best method to hit paydirt in this endervour. Analysts utilize numerous methodologies as well as employ a variety of hypotheses to solve this artful task, but not any has accomplished with a satisfactory margin of errors. The need to make informed choices causes correct and accurate information to be a desired and highly valued commodity. One encouraging Artificial Intelligence technique is the Long Short-Term Memory (LSTM) technique. Although LSTM is excellent in recognizing patterns, it places no considerable importance to the intense relationship that connects the past and its ensuing values within a time series. It does not take into account the association between the past and the later values. This research offers an optional LSTM method which contemplates the past, current and subsequent prices by predicting the Three-Point Moving Gradient of the stock market prices. The precise forecast price can be calculated with adequate accuracy by employing reversed Linear Regression (LR) and its mean price. The results is just as precise as the conventional LSTM method.