I have time series data for various assets and which I transform to create various features. I have framed the problem as a classification task where I attempt to predict either a positive or negative move above some threshold.

Example: If the next n day returns are above the threshold then label as a one. If below the threshold then a -1. If the threshold is not reached then a 0. (This is inline with much of what I see in the literature)

At this stage I have only investigated an SVM classifier but I am wondering if there are more appropriate models to use? If someone familiar with the literature could please point me to a few models that are common in forecasting financial time series, that would be great!

One idea I have had is the HM-SVM model architecture, am I getting warmer? Are there specific model tricks?

  • 1
    $\begingroup$ What method did you use with SVM? Where do you think it has problems? This is just too vague in the current state, you can edit it to make it generic (i.e. by making the strategy public) and I'll reopen it. $\endgroup$
    – SRKX
    Commented Oct 24, 2014 at 6:34
  • $\begingroup$ Let's give the improved version a try. $\endgroup$
    – Bob Jansen
    Commented Apr 4, 2019 at 13:51

2 Answers 2


From what I have read, there are 3 popular algorithms for financial time series. Random Forests and SVMs, then followed by Neural Network Architectures.

There are a couple of good papers, to name a few:

I have a lot of hope for the sequential models such as RNNs but I have had more success with Random Forests and SVMs.


I wrote a masters thesis related to machine learning in finance, and during this process I surveyed about 200 of the research papers that were written about the topic since 2018. This is the distribution of the algorithms used in the research papers:

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LSTM is by far the most used machine learning algorithm used to predict financial time series and used in just above 40% of the surveyed papers.

It seems like in both research articles as well as in code examples provided on different websites, LSTM is the go-to algorithm for predicting financial time series. LSTM usually gives excellent results when paired with the correct problems and datasets, although it is resource intensive. Another contender is Reinforcement learning, which is more popular when it comes to financial portfolios.

58% of the papers focused on only one algorithm, while 18% used two. 12% used 3 and 2% used 4. None used more than 4 different models.


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