Discussing the article: "Data Science and ML (Part 46): Stock Markets Forecasting Using N-BEATS in Python"
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Very good article thanks Omega.Check out the new article: Data Science and ML (Part 46): Stock Markets Forecasting Using N-BEATS in Python.
Author: Omega J Msigwa
Since it is utilising the fast fourrier transform for decomposition which allow model to capture both short-term seasonality and long-term trends separately. ,is using close price itself as input or output suitable for N-BEATS algorithm?
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Check out the new article: Data Science and ML (Part 46): Stock Markets Forecasting Using N-BEATS in Python.
N-BEATS is a revolutionary deep learning model designed for time series forecasting. It was released to surpass classical models for time series forecasting such as ARIMA, PROPHET, VAR, etc. In this article, we are going to discuss this model and use it in predicting the stock market.
N-BEATS (Neural Basis Expansion Analysis for Time Series) is a deep learning model specifically designed for time series forecasting. It provides a flexible framework for univariate and multivariate forecasting tasks.
It was introduced by researchers at Element AI (now part of ServiceNow) in 2019, with the paper N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.
Developers at Element AI developed this model to challenge the dominance of classical statistical models like ARIMA and ETS in time series, while compromising the capabilities offered by classical machine learning models.
We all know that time series forecasting is a challenging task, so machine learning experts and users sometimes rely on deep learning models such as RNNs, LSTMs, etc., which are often:
Meanwhile, traditional models for time series forecasting, like ARIMA, are often too simple for many tasks.
So, the authors/developers decided to make a deep learning model for time series forecasting that works well, is interpretable, and needs no domain-specific tweaks.
Author: Omega J Msigwa