Cryptocurrency Closing Price Prediction Challenge by IEEE ENSI SB
1630 DT
Can you predict the closing price for a cryptocurrency?
85 data scientists enrolled, 58 on the leaderboard
CryptocurrencyPredictionStructured
Tunisia
9 April—10 April
12 hours

This is a private hackathon open to all registered participants. If you are an intermediate and would like to participate, contact IEEE ENSI SB.

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After the boom and bust of cryptocurrencies’ prices in recent years, cryptocurrencies have been increasingly regarded as an investment asset. Because of their highly volatile nature, there is a need for good predictions on which to base investment decisions. Different existing studies have leveraged machine learning for more accurate cryptocurrency price prediction. We are interested in applying different modeling techniques to samples with different data structures (qualitative and quantitative data) and dimensional features to achieve an optimization in price prediction.

Using the trading time series of a cryptocurrency’s price, in addition to a set of qualitative features (news, social impact, Twitter, Reddit, social media sentiment analysis), we would like to build a model that forecasts a cryptocurrency’s price. In this challenge, we are focusing on the trading time series and how we can optimize currency forecasting. We will predict future cryptocurrency prices.

There are many factors and constraints that can be taken into consideration when increasing or decreasing cryptocurrency prices by the different stakeholders. These factors can be directly seen in newspapers, related websites or social media, for that including these features in the model can add value and predict more accurate cryptocurrency prices.

The target value is the actual price. We have data extracted in an interval of 1h for a period of one year (from 1st of March 2020 to 1st of March 2021). We are interested to predict the values of cryptocurrency prices in specific timestamps that we have in the validation file.

There can be different ways to solve this problem. One can think about using the prices from the different trading platforms as the initial data (the provided dataset) and build forecasting models and/or Neural Networks ones.

The participant that will build a model with the most accurate results will be the winning one.

The challenge does not stop there, as our main goal is to reach and exceed a given threshold (a specified RMSE score) in the final developed model.

The goal is to have predictions that are accurate in a way that it’s mostly similar to the original validation file, to bypass the given threshold evaluation result, and come up with something that is more accurate.

About EEE ENSI Student Branch (facebook.com/IEEE.ENSI.SB)

IEEE ENSI SB was created in October 2011 by engineering students from the national school of computer science, given the internationalization of this institute and the necessity that we have touched to integrate our school in such a movement that takes care of the improvement of the Engineering, computer science and information technology around the world. Our SB always tries to address the most relevant technical themes of today at local and global level through lectures and regular articles. Programs are often organized to ensure the growth of skills and knowledge among students and to encourage individual commitment to continuing education among IEEE volunteers. It comprises a range of competent, ambitious and serious engineering students who support IEEE mission to promote technology for humanity and the profession, while membership provides a platform for introducing careers Technology for students around the world.

About Think.iT Labs SARL (think-it.io)

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Our engineers build international careers with groundbreaking technologies at our state-of-the-art local HQ — while our partners access highly qualified and motivated talent without the cost and competition of local hiring.

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