Time Series Lab product range

Everything you need for state space modelling

Basic
  • Free, even for commercial use
  • Easy to use interface
  • Extract unobserved components like Level, Slope, Seasonal, and Cycle
  • Analyse the effect of Explanatory variables on your forecasts
  • Advanced Kalman filter and Smoother algorithms
  • Highly efficient code engine
  • Compare models on forecasting ability
Extended
With the extended package you have everyting you need for advanced univariate time series analysis. It includes everything from the Basic module

  • No limit on time series length
  • No limit on number of explanatory variables
  • Three seasonal components to model complex seasonal patterns
  • Three cycle components
  • Optimality algorithm for seasonal components
  • Automatic intervention analysis
  • Exact initialisation
  • And more...
Sequentially univariate
With the sequentially univariate package you have everyting you need to analyse large numbers of univariate time series. It includes everything from the Basic module

  • No limit on number of sequentially analyzed univariate time series
  • Immediately see which time series require extra modelling attention
  • Detect patterns shared across the time series
  • Batch processing module
  • Quickly store forecasts of all time series
  • And more...
Multivariate
With the multivariate package you have everyting you need for advanced multivariate time series analysis. It includes everything from the Basic module

  • Capture the dynamics of multiple time series simultaneously
  • Factor modelling for large panels of time series
  • Cointegration analysis
  • Batch processing module
  • And more...
Basic
  • Free, even for commercial use
  • Easy to use interface
  • Extract unobserved components like Level, Slope, Seasonal, and Cycle
  • Analyse the effect of Explanatory variables on your forecasts
  • Advanced Kalman filter and Smoother algorithms
  • Highly efficient code engine
  • Compare models on forecasting ability
Extended
With the extended package you have everyting you need for advanced univariate time series analysis. It includes everything from the Basic module

  • No limit on time series length
  • No limit on number of explanatory variables
  • Three seasonal components to model complex seasonal patterns
  • Three cycle components
  • Optimality algorithm for seasonal components
  • Automatic intervention analysis
  • Exact initialisation
  • And more...
Sequentially univariate
With the sequentially univariate package you have everyting you need to analyse large numbers of univariate time series. It includes everything from the Basic module

  • No limit on number of sequentially analyzed univariate time series
  • Immediately see which time series require extra modelling attention
  • Detect patterns shared across the time series
  • Batch processing module
  • Quickly store forecasts of all time series
  • And more...
Multivariate
With the multivariate package you have everyting you need for advanced multivariate time series analysis. It includes everything from the Basic module

  • Capture the dynamics of multiple time series simultaneously
  • Factor modelling for large panels of time series
  • Cointegration analysis
  • Batch processing module
  • And more...

Time Series Lab - State Space Edition features

Topic Basic Extended Sequentially Multivariate
Free download
Univariate time series analysis
User friendly Graphical User Interface
Advanced Kalman filter and Smoother algorithms
Analyse the effect of explanatory variables on your time series
Missing values are handled with ease
Advanced graphing capabilities
Model forecast comparison
No limit on time series length
No limit on number of explanatory variables
Number of seasonal components 1 3 3 3
Number of cycle components 1 3 3 3
Optimality algorithm for seasonal components
Advanced explanatory variable treatment*
Automatic outlier and break detection
Sequentially analyse large numbers of time series**
Analyse multiple series in one system***
* This includes the modules lag finder and automatic variable selection and the possibility to restrict and handle explanatory variables in a variety of ways.
** This is often used if large numbers of univariate time series need to be forecasted, often on a regular basis. You can think of the forecasting of sales products for example.
*** Capture the dynamics of multiple time series simultaneously. This includes the modelling of large systems with the use of factor models.

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 Bio
Professor 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 website
Professor 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 website
P. 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 website

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