Discussing the article: "Data Science and Machine Learning (Part 24): Forex Time series Forecasting Using Regular AI Models"
You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
Check out the new article: Data Science and Machine Learning (Part 24): Forex Time series Forecasting Using Regular AI Models.
In the forex markets It is very challenging to predict the future trend without having an idea of the past, Very few machine learning models are capable of making the future predictions by considering past values. In this article, we are going to discuss how we can use classical(Non-time series) Artificial Intelligence models to beat the market.
Unlike classic machine learning models such as Linear Regression, Support Vector Machine (SVM), Neural Networks (NN) and others, that we have discussed in prior articles—which aim to determine relationships among feature variables and make future predictions based on these learned relationships—time series models forecast future values based on previously observed values.
This difference in approach means that time series models are specifically designed to handle temporal dependencies and patterns inherent in sequential data. Time series forecasting models, such as ARIMA, SARIMA, Exponential Smoothing, RNN, LSTM, and GRU, leverage historical data to predict future points in the series, capturing trends, seasonality, and other temporal structures.
Author: Omega J Msigwa