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 specialised expertise while at the same time there are many algorithms available for analysis and modelling.
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 analyse 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 world's first AI-driven virtual data scientist, capable of understanding large scale time series data with minimal human effort.
Bio: Dr. Darko Matovski is the CEO of causaLens. The company provides automated machine learning solution for time-series predictions and serves prominent organisations including hedge funds and asset managers. Darko has also worked for cutting edge hedge funds and research institutions. For example, the National Physical Laboratory in London (where Alan Turing worked) and Man Group in London. Darko has a PhD in Machine Learning and an MBA.