# Tag Archives: automatic specification

## Complex Exponential Smoothing

A couple of days ago my ex-student Ivan Svetunkov successfully defended his PhD. My thanks to both Siem Jan Koopman and Rebecca Killick who were his examiners and with their questions  led to a very interesting discussion. Ivan’s PhD topic is a new model, the Complex Exponential Smoothing (CES). In this post I will very… Read More »

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 »

## How to choose a forecast for your time series

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 fit an elephant?

I was looking for an intuitive way to demonstrate to my students the need for parsimony in model building, as well as the problem of overfitting and I remembered the humorous paper by James Wel: showing that elephants are obviously created by Fourier sine series! I went a step further and implemented some popular selection… Read More »

## Additive and multiplicative seasonality – can you identify them correctly?

Seasonality is a common characteristic of time series. It can appear in two forms: additive and multiplicative. In the former case the amplitude of the seasonal variation is independent of the level, whereas in the latter it is connected. The following figure highlights this: Note that in the example of multiplicative seasonality the season is… Read More »

## Choosing parameters for Croston’s method and its variants

Although Croston’s method and its variants are popular for intermittent demand time series, there have been limited advances in identifying how to select appropriate smoothing parameters and initial values. From the one hand this complicates forecasting for organisations, and from the other hand it does not permit automation. Recent research investigated various cost functions for… Read More »

## MAPA and intermittent demand forecasting

Recently I posted about a paper I co-authored with Fotios Petropoulos, now in JORS: Forecast Combinations for Intermittent Demand. There we found that for intermittent demand data using multiple levels of temporal aggregation, forecasting them with the appropriate models and finally combining the forecasts performed best. This approach has many analogies with the MAPA algorithm… Read More »

## Improving forecasting by estimating time series structural components across multiple frequencies

N. Kourentzes, F. Petropoulos and J. R. Trapero, 2014, International Journal of Forecasting, 30: 291-302. http://dx.doi.org/10.1016/j.ijforecast.2013.09.006

## Data Driven Fitting Sample Selection For Time Series Forecasting With Neural Networks

N. Kourentzes, 2012, International Joint Conference on Neural Networks, Brisbane, 10-15 June 2012.