Time Series Lab Software
Time Series Lab is the name of a family of software programs designed to model and forecast structural time series models. These models use advanced techniques but are designed to be easy to use. At the most basic level all that is required is some appreciation of the concepts of trend, seasonal and irregular. The hard work is done by the program, leaving the user free to concentrate on formulating models and using them to make forecasts.
Time Series Lab Research
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 including economics, finance, sports, climatology, biology, and health science.
Time Series Lab Consultancy
Time Series Lab offers consultancy services to companies who require data analysis that go beyond what is offered by our software packages. We advise on data analysis methods and implementation of them. Time Series Lab also designs software packages specifically suited to your company.
TSL Sports Statistics
The Time Series Lab team dives into the world of Sports Statistics. We developed a new package called Time Series Lab - Sports Statistics Edition. This is a software program for analysing, modelling, and forecasting of time series focusing on sports results. The software allows you to choose from several probability distributions and model specifications to extract sports team strengths from your data. The software is based on football models that appeared in the academic literature.
Time Series Lab Engine
The engine behind our State Space Edition software is a set of C routines with the name SsfPack. We pride ourselves in having extremely efficient State Space algorithms that result in fast optimisation times even for large systems. When working with large datasets our optimised algorithms are an important asset in keeping estimation times as low as possible.
Time Series Lab product range
A free and advanced time series forecasting package
Home Edition
- Free, even for commercial use*
- Easy to use interface
- Many different time series models ranging from basic to expert level
- Analyse the effect of Explanatory variables on your forecasts
- Highly efficient code engine
- Compare models on forecasting performance
- Model complex seasonal patterns, for example hour-of-day, day-of-week, and month-of-year patterns
- Machine learning algorithms for outlier and break detection
- User manual including Case studies to quickly learn how to use Time Series Lab for modelling and forecasting
- And much more...
Enterprise Edition
With the Enterprise Edition you have everyting you need to analyse large numbers of univariate time series. The Enterprise Edition includes everything from the Home Edition
- No limit on number of sequentially analyzed univariate time series**
- See which time series require extra modelling attention
- Detect patterns shared across the time series
- Batch processing module
- Program TSL rather than go through all modelling steps and menus manually
- Schedule TSL, for example to run the Batch program every morning at 07:00 or to run the Batch program repeatedly every 60 seconds.
- Quickly store forecasts of all time series
- Consultancy services
Home Edition
- Free, even for commercial use*
- Easy to use interface
- Many different time series models ranging from basic to expert level
- Analyse the effect of Explanatory variables on your forecasts
- Highly efficient code engine
- Compare models on forecasting performance
- Model complex seasonal patterns, for example hour-of-day, day-of-week, and month-of-year patterns
- Machine learning algorithms for outlier and break detection
- User manual including Case studies to quickly learn how to use Time Series Lab for modelling and forecasting
- And much more...
Enterprise Edition
With the Enterprise Edition you have everyting you need to analyse large numbers of univariate time series. The Enterprise Edition includes everything from the Home Edition
- No limit on number of sequentially analyzed univariate time series**
- See which time series require extra modelling attention
- Detect patterns shared across the time series
- Batch processing module
- Program TSL rather than go through all modelling steps and menus manually
- Schedule TSL, for example to run the Batch program every morning at 07:00 or to run the Batch program repeatedly every 60 seconds.
- Quickly store forecasts of all time series
- Consultancy services
* Time Series Lab should be cited whenever it is used in the following way: Lit, R., S.J. Koopman, and A.C. Harvey (2022), Time Series Lab: https://timeserieslab.com
** Limitations on time and hardware can always be a limiting factor
Our team
Developers with a close connection to academia
R. Lit, PhD
Rutger Lit is a research fellow at the Vrije Universiteit Amsterdam and has a PhD in econometrics, specialising in time series analysis. In 2017, he founded Nlitn, a company offering consultancy services. It also offers full data solution packages to meet the data analysis needs of clients. For example, the Time Series Lab software series.
Short BioProfessor S.J. Koopman
Siem Jan Koopman is Professor of Econometrics at the Department of Econometrics and Data Science, Vrije Universiteit Amsterdam. He is also a research fellow at Tinbergen Institute and a long-term Visiting Professor at CREATES, University of Aarhus.
He held positions at London School of Economics and CentER (Tilburg University), and had long-term visits at US Bureau of the Census, European University Institute, and European Central Bank, Financial Research.
Academic websiteProfessor A.C. Harvey
Andrew Harvey is Emeritus Professor of Econometrics in the Faculty of Economics and Politics, University of Cambridge. He was Professor of Econometrics at the London School of Economics before coming to Cambridge in 1996. His most recent book is a monograph entitled Dynamic Models for Volatility and Heavy Tails. Andrew Harvey is also a Fellow of the Econometric Society and a Fellow of the British Academy.
Academic websiteP. Gorgi, PhD
Paolo Gorgi is Associate Professor at the department of Econometrics and Data Science, Vrije Universiteit Amsterdam. He obtained a double PhD in statistics and econometrics from the University of Padova and Vrije Universiteit Amsterdam. His main research interests concern: time series analysis, statistical inference for dynamic models and forecasting economic variables. He has published articles in several academic journals, including Journal of the Royal Statistical Society: Series B and Journal of Econometrics.
Academic websiteGet in touch
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