Stock Price Prediction: A Time Series Analysis

Abstract

Predicting future stock volatility has always been a demanding chore for research studies. Individuals around the world have long regarded the stock market as a substantial profit. A stock data set contains numerous precise terms that are difficult for an individual to comprehend when considering stock market expenditures. An essential manifestation of a stock’s performance on the stock market is its closing price, but it is challenging to estimate the stock market’s price movements. This study aims to provide a future market scenario supported by statistical data. We used the Microsoft Corporation Stock dataset from 1986 to 2022. To foresee stock market volatility, we used time series analysis with the Long Short-Term Memory (LSTM), Bidirectional Long-short Term Memory (Bi-LSTM), Autoregressive Integrated Moving Average (ARIMA), Hidden Markov Model (HMM), and Multi-Head Attention. We have achieved 0.153, 0.202, 6.674, 14.760, and 21.493 for Transformer, HMM, ARIMA, BiLSTM, and LSTM respectively.