Reproducibility in computational research

Jane Frazier spoke at our research team meeting today on “Reproducibility in computational research”. We had a very stimulating and lively discussion about the issues involved. One interesting idea was that reproducibility is on a scale, and we can all aim to move further along the scale towards making our own research more reproducible. For example

  • Can you reproduce your results tomorrow on the same computer with the same software installed?
  • Could someone else on a different computer reproduce your results with the same software installed?
  • Could you reproduce your results in 3 years time after some of your software environment may have changed?
  • etc.

Think about what changes you need to make to move one step further along the reproducibility continuum, and do it.

Jane’s slides and handout are below. Continue reading →

Upcoming talks in California

I’m back in California for the next couple of weeks, and will give the following talk at Stanford and UC-Davis.

Optimal forecast reconciliation for big time series data

Time series can often be naturally disaggregated in a hierarchical or grouped structure. For example, a manufacturing company can disaggregate total demand for their products by country of sale, retail outlet, product type, package size, and so on. As a result, there can be millions of individual time series to forecast at the most disaggregated level, plus additional series to forecast at higher levels of aggregation.

A common constraint is that the disaggregated forecasts need to add up to the forecasts of the aggregated data. This is known as forecast reconciliation. I will show that the optimal reconciliation method involves fitting an ill-conditioned linear regression model where the design matrix has one column for each of the series at the most disaggregated level. For problems involving huge numbers of series, the model is impossible to estimate using standard regression algorithms. I will also discuss some fast algorithms for implementing this model that make it practicable for implementing in business contexts.

Stanford: 4.30pm, Tuesday 6th October.
UCDavis: 4:10pm, Thursday 8th October.

Mathematical annotations on R plots

I’ve always struggled with using plotmath via the expression function in R for adding mathematical notation to axes or legends. For some reason, the most obvious way to write something never seems to work for me and I end up using trial and error in a loop with far too many iterations.

So I am very happy to see the new latex2exp package available which translates LaTeX expressions into a form suitable for R graphs. This is going to save me time and frustration! Continue reading →

Murphy diagrams in R

At the recent International Symposium on Forecasting, held in Riverside, California, Tillman Gneiting gave a great talk on “Evaluating forecasts: why proper scoring rules and consistent scoring functions matter”. It will be the subject of an IJF invited paper in due course.

One of the things he talked about was the “Murphy diagram” for comparing forecasts, as proposed in Ehm et al (2015). Here’s how it works for comparing mean forecasts. Continue reading →