AI in investing

Joint Event on 5^th World Machine Learning and Deep Learning Congress and World Congress on Computer Science, Machine Learning and Big Data

August 30-31, 2018 Dubai, UAE

Darko Matovski

causaLens, UK

Posters & Accepted Abstracts: J Comput Sci Syst Biol

Abstract :

Measurements about economic activity are primarily represented in the form of time-series data. This is always the case for real-time data generated from connected devices. Time-series data is unique due to the presence of temporal characteristics. Building models using this type of data is difficult and requires specialized expertise while at the same time there are many algorithms available for analysis and modeling. Connected devices generate time series, i.e. data that shows how a particular variable or measurement changes over time. For the purposes of building an economic model, data of any kind (text, images, voice, video, etc.) ultimately gets transformed into the form of time series. However, there are few tools to effectively analyze this sort of data using next-generation technology like machine learning and AI. Until now, economic researchers have used structural models and econometric models developed in the previous century. The performance of these models has been underwhelming and plagued with lack of robustness and small sample sizes. Modern algorithms, such as machine learning and new sources of data stand to completely change the face of economic modeling and forecasting. Yet, the adoption of modern AI models in economic research has been slow, primarily due to lack of technology and talent. causaLens has developed the worldâ??s first AI-driven virtual data scientist, capable of understanding large scale time series data with minimal human effort.

Biography :

E-mail: apostolos@causalens.com