Time Series Lab

Find the signal in your time series.

Time Series Lab Articles

The Time Series Lab - Article Series are dedicated to research performed with Time Series Lab software. The scope of the series includes the analysis and forecasting of a wide range of time series in fields like economics, finance, sports, climatology, biology, and health science.

Joint Editors:
A.C. Harvey
S.J. Koopman
R. Lit

For information regarding the publication of papers please contact us.

Time Series Lab should be cited whenever it is used in the following way: Lit, R., S.J. Koopman, and A.C. Harvey (2019-2021), Time Series Lab: https://timeserieslab.com.

Time Series Lab - Sports Statistics Edition should be cited whenever it is used in the following way: Lit, R. (2019-2020), Time Series Lab - Sports Statistics Edition: https://timeserieslab.com.

Articles

Articles

October 5, 2020

Estimation of final standings in football competitions with premature ending: the case of COVID-19

By Paolo Gorgi, Siem Jan Koopman, and Rutger Lit

  |   PDF   |   Citation   |   Data

We study an alternative approach to determine the final league table in football competitions with a premature ending. For several countries, a premature ending of the 2019/2020 football season has occurred due to the COVID-19 pandemic. We propose a model-based method as a possible alternative to the use of the incomplete standings to determine the final table. This method measures the performance of the teams in the matches of the season that have been played and predicts the remaining non-played matches through a paired-comparison model. The main advantage of the method compared to the incomplete standings is that it takes account of the bias in the performance measure due to the schedule of the matches in a season. Therefore, the resulting ranking of the teams based on our proposed method can be regarded as more fair in this respect. A forecasting study based on historical data of seven of the main European competitions is used to validate the method. The empirical results suggest that the model-based approach produces more accurate predictions of the true final standings than those based on the incomplete standings.

June 8, 2020: Updated version

Forecasting the VIX in the midst of COVID-19

By Rutger Lit

  |   PDF   |   Citation   |   Supplemental   |  

April 14, 2020

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We study the behavior of the Volatility Index (VIX) time series in the period leading up to the COVID-19 outbreak. Time-varying location/scale models are used to extract a range of time-varying components from the VIX time series. The time-varying components are driven by the score of the predictive density. These so called score-driven models have proven to be powerful in extracting time-varying components like autoregressive processes and seasonal patterns. A range of model specifications is used to forecast the VIX in the COVID-19 period that spans the first quarter of 2020. Explanatory variables are used to improve in-sample model fit and out-of-sample forecast accuracy. All model computations are carried out with the Time Series Lab software package.

May 18, 2020

Coronavirus and the Score-driven Negative Binomial Distribution

By Andrew Harvey and Rutger Lit

  |   PDF   |   Citation   |   Data

A new class of time series models, developed by Harvey and Kattuman (2020), is designed to predict variables which when cumulated are subject to an unknown saturation level. Such models are relevant for many disciplines, but the applications here are for deaths from coronavirus. When numbers are small a score-driven Negative Binomial model can be used. It is shown how such models can be estimated with the Time Series Lab software package and their specification assessed by statistical tests and graphics.

May 11, 2020

Forecasting the 2020 edition of the Boat Race

By Rutger Lit and Siem Jan Koopman

  |   PDF   |   Citation

We study the annual outcome of the Boat Race between Oxford and Cambridge and forecast the 2020 edition which was cancelled due to the COVID-19 outbreak. We find a strong presence of cyclical behaviour in the time series dynamics and model it through an autoregressive process with score-driven innovations. The inclusion of explanatory variables improve the fit of the time series further. In particular, the weight difference between the rowers in the boats of the two universities is a statistically significant predictor. All model computations are performed with the Time Series Lab software package and can be easily replicated.