Seasonal versus non-seasonal trends in stock market Malaysia

Stock market prediction is considered a challenging task of financial time series analysis, which beneficial for investors, stock traders, and future researchers. In Malaysia, many machine learning techniques have been used price such as Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM). This study will use ARIMA Seasonal to present weekly, monthly quarterly predictions, both with without seasonal adjustment method. movement are presented using weekly data six industries in Malaysia gloves, property, airlines, banking, oil gas, pharmaceuticals from 26th September 2016 until 28th 2020. The principle objective this verify non-seasonal occurs the Malaysian demonstrate improvement predictive performance market.