Internship Topics

Prof. Helmut Prendinger

Thank you for your interest in our topics!

Objective:

The Objective of the Internship is a tangible result, such as:

· Research results that stand a chance of publication in a major venue (journal or conference), whereby the Intern assumes authorship or co-authorship

· Development or co-development of competitive software, or an intelligent system that can be demonstrated

Paper writing will be supported by Prendinger Lab.

Topic 1:

Research Area: Deep Learning/Time Series

Title of Research Topic: Time Series Analysis for Bitcoin Market Prediction

We aim to understand the predictability of cryptocurrencies, such as Bitcoin or Ethereum.

Our goal is to investigate Deep Learning (DL) models and other methods (e.g., Convergent Cross Mapping), for predicting the price of Bitcoin and other cryptocurrencies for successful swing trading.

We consider several DL models for time series forecasting, such as:

· Transformers

· Retentive Network

· Echo State Network

We apply several methods for analyzing causality between time series, such as:

· Transfer Entropy, Convergent Cross Mapping

We use different kinds of input:

· Candlestick chart

· Indicators, such as Relative Strength Index (RSI)

· S&P500, Gold, …

· Etc.

Key references:

Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, Furu Wei, Retentive Network: A Successor to Transformer for Large Language Models, 2023, https://arxiv.org/abs/2307.08621

Ailing Zeng, Muxi Chen, Lei Zhang, Qiang Xu, Are Transformers Effective for Time Series Forecasting? 2022, https://arxiv.org/abs/2205.13504

Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long, Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting, 2022, https://arxiv.org/abs/2106.13008

Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, Wancai Zhang, Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting, 2021, https://arxiv.org/abs/2012.07436

Patrick Jaquart, David Dann, Christof Weinhardt, Short-term bitcoin market prediction via machine learning. The Journal of Finance and Data Science, Vol. 7, 2021, 45-66

https://www.sciencedirect.com/science/article/pii/S2405918821000027?via%3Dihub

George Sugihara, Robert May, Hao Ye, Chih-Hao Hsieh, Ethan Deyle, Michael Fogarty, and Stephan Munch. Detecting causality in complex ecosystems. Science, 338:496 – 500, 2012 https://www.science.org/doi/10.1126/science.1227079

Topic 2:

Research Area: Large Language Models (LLM), ChatGPT

Title of Research Topic: Large Language Models for Bitcoin Market Prediction

We aim to understand the potential of LLMs for predicting the price action of Bitcoin. The success of ChatGPT has inspired LLMs dedicated to finance, such as BloombergGPT or FinGPT. We want to investigate how LLMs can complement Deep Learning based technical chart analysis for improving the accuracy of Bitcoin price development. We also want to study attempts to directly use LLMs to improve time series forecasting, e.g. LLM4TS.

Key references:

FinGPT, https://github.com/AI4Finance-Foundation/FinGPT

Ching Chang, Wen-Chih Peng, Tien-Fu Chen, LLM4TS: Two-Stage Fine-Tuning for Time-Series Forecasting with Pre-Trained LLMs, 2023, https://arxiv.org/abs/2308.08469

Hongyang Yang, Xiao-Yang Liu, Christina Dan Wang, FinGPT: Open-Source Financial Large Language Models, 2023, https://arxiv.org/abs/2306.06031

Alejandro Lopez-Lira and Yuehua Tang, Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models, 2023, https://arxiv.org/pdf/2304.07619.pdf

Ekin Tiu, Ellie Talius, Pujan Patel, Curtis P. Langlotz, Andrew Y. Ng & Pranav Rajpurkar, Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning, Nature Biomedical Engineering volume 6, pages 1399–1406 (2022), https://www.nature.com/articles/s41551-022-00936-9

Topic 3:

Research Area: Token Economy, Blockchain

Title of Research Topic: Market Design for Unmanned Aircraft Systems ("drone") Traffic Management (UTM)

We are developing a prototype of the entire UTM system to manage drone air traffic, and conduct related simulation studies.

· Video: https://youtu.be/QuIEdMqFJqw

Recently, we got interested in market design for UTM, based on ideas from token economy (Web3). The project aims to investigate the feasibility of introducing a UTM related token to the UTM eco-system.

Key references:

Sven Seuken, Paul Friedrich, Ludwig Dierks, Market design for drone traffic management. AAAI, 2022, https://ojs.aaai.org/index.php/AAAI/article/view/21493

Shermin Voshmgir, Token Economy. How the Web3 reinvents the Internet, Second Edition, 2020, https://github.com/sherminvo/TokenEconomyBook

Contact

E-mail: helmut @ nii [Address format is: username@nii.ac.jp]

Personal Website: http://research.nii.ac.jp/~prendinger/

LOOKING FORWARD TO HEARING FROM YOU!