A deep learning framework for financial time series using stacked autoencoders and long-short term memory

(Last Updated On: January 4, 2018)

A deep learning framework for financial time series using stacked autoencoders and long-short term memory

Most accurate machine deep learning model type?

This appears to use LSAM and SAE (long short term memory and stacked auto encoders) which appears to be more accurate than recurrent neural network (RNN).  Do I sound like a machine learning experience here? Haha. I am way off it. This does show that this technique could be most accurate when it comes to forecasting financial time series.

Here are the links

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0180944

Python code example https://github.com/dzitkowskik/StockPredictionRNN

https://en.wikipedia.org/wiki/Long_short-term_memory

 

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About caustic

Hi i there My name is Bryan Downing. I am part of a company called QuantLabs.Net This is specifically a company with a high profile blog about technology, trading, financial, investment, quant, etc. It posts things on how to do job interviews with large companies like Morgan Stanley, Bloomberg, Citibank, and IBM. It also posts different unique tips and tricks on Java, C++, or C programming. It posts about different techniques in learning about Matlab and building models or strategies. There is a lot here if you are into venturing into the financial world like quant or technical analysis. It also discusses the future generation of trading and programming Specialties: C++, Java, C#, Matlab, quant, models, strategies, technical analysis, linux, windows P.S. I have been known to be the worst typist. Do not be offended by it as I like to bang stuff out and put priorty of what I do over typing. Maybe one day I can get a full time copy editor to help out. Do note I prefer videos as they are much easier to produce so check out my many video at youtube.com/quantlabs