Version 1.00 is the newest version of Time Series Lab - Score Edition. Currently, Windows 64 bit is the only supported platform.

A few users have reported a warning message during installation. Time Series Lab - Score Edition is a new software product. It is therefore not yet recognised by Windows as a known app. Windows Defender SmartScreen, a feature that prevents unrecognized apps from running, might tell you that:

"Windows Defender Smartscreen prevented an unrecognised app from starting".

If you encounter this warning message, click the "More info" link at the end of the warning paragraph. You see a new window with a new option: Run anyway.
Of course you can always run the program by a virus scanner after which you will see that Time Series Lab - Score Edition is not malware.

We go live soon!


Version 1.00

  • New outline of "Model setup" and "Advanced settings" menu
  • Fixed bug regarding the identification of two AR components
  • Fixed inefficiency in likelihood calculation
  • Added functionality where the user can select the time series axis from the loaded database
  • Right mouse click on graphs for additional options on "Graphics" and "Forecasting" page
  • More elaborate description of estimated model on "Main menu" page
  • Functionality added to update the program without the need of manually downloading the newest install file

Version 0.40

  • Diagnostic tests added
  • Interactive forecasting where losses from several loss functions are reported for user-selected forecast windows
  • Addition of probability distributions "Exponential Generalized Beta 2", "Exponential", and "Bernoulli"
  • Addition of leverage effect in scale
  • Tooltip messages are added to inform the user about functionalities

Version 0.30

  • Addition of second autoregressive component
  • ACF and PACF plotting functionality on "Load data" page
  • Interactive model description on "Model setup" page
  • Significance levels included in parameter report

Version 0.20

  • Graphical capabilities added to the "Load data" page
  • Data transformations can be applied to the data
  • Addition of explanatory variables for location and scale
  • User can select a range (t1-t2) from the time series to estimate