DataScienceBites | Ideas | Real World Data Science – Real World Data Science

Nowcasting upgrade for better real time estimation of GDP and inflation

Forecasting

Machine learning

Atmajitsinh Gohil describes a recently devised 3 step framework that improves the accuracy of nowcasting.

Atmajitsinh Gohil

Heading to a conference this summer? Share your learnings here

Content ideas

Call for contributions

Events

Calling all summer conference delegates! If you’re heading to one of this year’s big statistics and data science events, consider writing about your favourite paper or session as a ‘Bites’ post. Read on for more details…

Choosing the right forecast

Forecasting

Prediction

Robustness

Practitioners often select forecast methods based on averages of scores from many probabilistic forecasts. But this is not without its difficulties. A recent paper by Bolin and Wallin defines several properties of scoring rules that help clarify how the rules behave when multiple forecast scores are averaged. Brian King shares his key takeaways.

Using ‘basket complementarity’ to make product recommendations

Market basket analysis

Recommendation systems

Complementarity

Purchase suggestions – e.g., “if you are buying that, you might also want this” – are, to a large extent, informed by the concept of complementarity: that certain products are often bought and/or used together. A journal paper by Puka and Jedrusik sheds light on how these product recommendations can be derived, as Moinak Bhaduri explains.

Pulling patterns out of data with a graph

Data management

Dimension reduction

Graph theory

Large volumes of data are pouring in every day from scientific experiments, so much so that it is now commonplace to perform dimension reduction in order to reduce a large number of measurements to a set of key values that are easier to visualize and interpret. Enter ‘The Sequencer’, a proposed method to find trends within high-dimensional datasets.

Andrew Saydjari

Determining the best way to route drivers for ridesharing via reinforcement learning

A/B testing

Reinforcement learning

Statistics

A/B testing is often used to evaluate the impact of design ‘treatments’ — for example, are people who see advert A more likely to buy something than those who see advert B? Classical methods typically assume that changing one person’s treatment will not affect others, but what if that’s not the case? A paper by Shi et al. aims to address this problem.