Last February Facebook open sourced its Prophet forecasting tool. Since, it had appeared in quite a few discussions online. A good thing about Prophet is that it one can use it very easily through R (and Python). This gave me the opportunity to benchmark it against some more standard – and not! – forecasting models… Read More: Benchmarking Facebook’s Prophet »
The ABC-XYZ analysis is a very popular tool in supply chain management. It is based on the Pareto principle, i.e. the expectation that the minority of cases has a disproportional impact to the whole. This is often referred to as the 80/20 rule, with the classical example that the 80% of the wealth is owned… Read More: ABC-XYZ analysis for forecasting »
It has been a while since I got the time to post any updates, but finally got round to it. Last June I attended the International Symposium on Forecasting (ISF) at Santander, which had been very interesting and enjoyable. I had already posted about the presentations I gave. I was involved in a number of… Read More: ISF 2016 research presentations »
Issue 41 of Foresight featured a short commentary by Sujit Singh on the gaps between academia and business. Together with Fotios Petropoulos, motivated by our focus to produce and disseminate research that is directly applicable to practice, in this commentary we present our views on some of the very useful and interesting points raised by… Read More: Academia vs. Business: Two Sides of the Same Coin »
Last week I attended the International Symposium on Forecasting 2016. It was very interesting and enjoyable. Apart from the workshop on forecasting with R, I gave the following presentations: Forecasting with Temporal Hierarchies This presentation was given to the practitioner track of the conference and its aim was to introduce the basic idea of temporal… Read More: International Symposium on Forecasting Presentations »
Choosing the most appropriate forecasting method for your time series is not a trivial task and even though there has been scientific forecasting for so many decades, how to best do it is still an open research question. Nonetheless, there are some reasonable ways to deal with the problem, which although they may not be… Read More: How to choose a forecast for your time series »
In this post I introduce a new bias metric that has several desirable properties over traditional ones. When evaluating forecasting performance it is important to look at two elements: forecasting accuracy and bias. Although there has been substantial progress in the measurement of accuracy with various metrics being proposed, there has been rather limited progress… Read More: The Bias Coefficient: a new metric for forecast bias »
I recently became aware of a new forecasting competition: “International Forecasting Competition – Computational Intelligence in Forecasting”. The competition involves forecasting 91 time series of annual, quarterly, monthly and daily sampling frequency of various lengths. Although the competition is focused on computational intelligence methods (incl. fuzzy method, artificial neural networks, evolutionary algorithms, decision & regression… Read More: Time series forecasting competition with computational intelligence methods »
Over the years I have reviewed numerous papers that do not properly benchmark the various methods proposed. In my opinion if a paper has an empirical evaluation, then it must have appropriate benchmarks as well. Otherwise, one cannot claim that convincing empirical evidence is provided. The argument is simple: if the proposed method does not… Read More: Benchmark your forecasts »
The critical distance for the Nemenyi test is calculated as: , where is the confidence level, is the number of models and is the number of measurements. To calculate the Studentised range statistic for infinite degrees of freedom divided by is used. The values of for up to can be download here as csv. You… Read More: Critical values for the Nemenyi test »