Forecasting the stock market on the
financial and commodity markets is a huge problem for investors, businesses,
and speculators, but it could be fruitful if done well. Financial time series,
however, are among the most noisy and difficult to predict signals because of
the stock market’s basic swings, nonlinear behaviour, complexity, adaptability,
nonparametricity, and uncertainty. Corporate policies, political changes, general
economic conditions, and trader expectations are just a few of the
macroeconomic elements in finance that have an impact on stock market
movements. As a result, predicting changes in financial market values can be
The stock forecasting model typically
uses machine learning methods such as support vector machines (SVM), neural
networks (NN), models that combine them with other algorithms, and long
short-term memory (LSTM) as a deep learning tool. There have been numerous
studies using artificial neural networks (ANNs) in time-series modelling and
forecasting. ANNs have been used successfully in forecasting studies
encompassing a wide range of domains.
Any machine learning method would find
it extremely challenging because the network would need to learn a variety of
things that change over time. The LSTM network performed well despite this,
which is a significant advantage.
We may say that time series stock price
predictions using MLP and LSTM models are both effective. SVM still need improvement
for prediction applications, contrary to earlier studies that suggested it is a
substitute model for financial timeseries forecasting. The more training cases
we use,the better results we get in forecasting time series.
Future research is expected to
contribute to the literature by comparing MLP and LSTM models with other
large-scale data collected from diverse sectors and other methodologies used in
time series forecasting applications. This will help determine how effective
MLP and LSTM models are. We will develop a number of significant models for
forecasting financial time series based on the results of these new studies.
Building numerous machine learning
models for various variables, similar to the model employed but using a more
advanced method to combine the data, could be one feasible solution for the
For the final price projections, combine
various machine learning models into one.
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