Deep Learning Applied to Time Series Forecasting

Deep learning has been a major improvement on current machine learning technology. It has led to much more accurate machine translation, natural language processing, and summary creation. How does deep learning help with time series forecasting? It turns out that deep learning models do surprisingly very well with time series forecasting. In fact, forecasting accuracy is dramatically improved.

The major discovery has been that encoder-decoder frameworks have been very good at creating exceptional results. The performance and scalability of the Multi-Quantile Recurrent Forecaster has been exceptional. The overall architecture has meant that this type of forecasting was sped up dramatically. A deep neural network can do a lot of things in this domain, and it is only going to grow as people exploit how well it works. We also have other considerations, as there are other ways of doing things using deep learning. It is all about choosing the correct approach for your unique needs.

Understanding Deep Learning

Deep learning is a way of doing machine learning that simulates the way our brains work. It focuses on a deeper neural network that can configure itself to gain knowledge from scratch. Deep learning models are great because they enable machines to learn by themselves to practice by experimentation.

This technology has even been used by companies to create exceptional machines in certain video games. For example, an artificial intelligence company was able to use these techniques to build one of the best Go engines in the world. There were also able to do the same in a game like StarCraft II. This shows the potential in this technology, and it is exceptional for time series forecasting.

Compared to the Traditional Model

As with any other type of machine learning, the first step is to get a data set you can use to train your deep learning models on. There are standard machine learning models you can use, and those can be good enough for many people. The model is then able to predict the future based on recent history.

While this might seem inaccurate, you can do feature engineering to ensure that your data set becomes a lot better adapted to your current workflow. This will make your forecasting accuracy shoot up dramatically. Another big concern will be how well your time series forecasting model does over a longer period of time.

Applying It to Stock Forecasting

A major example of how this type of deep learning could be applied in industrial applications would be to the stock market. Stock forecasting can be a lucrative business if you get things right. However, major competitors will often have a handful of Ph.D. computer scientists and mathematicians trying to get the most accurate predictions possible. Your deep learning models should be tuned to ensure that they can make predictions as far forward as possible.

If this is a bit too technical for you, you might want to have something like xpresso.ai to implement your machine learning and deep learning projects. It makes the process of building an accurate model fast and easy.