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ForTANK

We are continously developing two versions of a prototype Forecasting Support System:

These software tools integrate all the state-of-the-art solutions developed by the experts of the forLAB as well as the broader forecasting think-TANK: forTANK, and are provided for free to Academics, affiliated Institutions and Commercial Partners

We also do keep on developing research applications:

  • HorsesforCourses Simulator, the software that replicates the simulated time series and their respective forecasts from the academic article "‘Horses for Courses’ in Demand Forecasting" from Dr. Fotios Petropoulos, Professor Spyros Makridakis, Professor Vassilios Assimakopoulos and Professor Konstantinos Nikolopoulos.
  • pLCC g-forCASTing tool v1.0 (Timeseries tool for Council Wards), v1.1(Timeseries tool for Council Postcodes) and v2.0(Causal NN tool for Case-by-Case); demo of the prototype Local City Council Strategic Forecasting tool may be requested from the CEO of Qoob Europe Ltd Mr Eugene Adams.

 

Data

Data from the article "Growth, deregulation and rent seeking in post-war British economy" , by S. P. Chakravarty, D. D. Thomakos and K. I. Nikolopoulos publishe din 2015 in Applied Economics

 

Freeware Software

Delphi is a judgmental forecasting software platform offering multi-round surveys of experts where anonymous feedback on the groups' responses and on reasoning is provided to the experts after each round.  For a quick  Delphi software user guide see.

R statsticial software Forecast and Package for R  provides tools to forecast using time-series data in Language R for Windows. It contains:

  • 24 exponential methods (smoothing in the state space modeling framework) from Hyndman, Koehler, Snyder and Grose, International Journal of Forecasting, 2002, 18, 439-454).
  • Automatic selection of model ARIMA
  • Graphical methods to show time series
  • Sets of data from Makridakis, Wheelwright and Hyndman (1998), Forecasting: methods and applications, Wiley & Sons: New York. See here & here.

PEERForecaster Add-in for Excel: A univariate time series forecasting package.

  • An implementation of the state-space models from Hyndman, Koehler, Snyder and Grose, International Journal of Forecasting, 2002, 18, 439-454.
  • Include all the well-known techniques from simple smoothing, Holt trending, Holt-Winters seasonal models, and damped trend exponential smoothing models to the Box Jenkins ARIMA models.
  • Would be useful to practitioners for benchmarking and validating comparable models found in expensive demand planning systems.
  • The algorithms and model interpretations are documented in Levenbach and Cleary (2005) Forecasting: Practice and Process for Demand Management, Cengage Publishers.

Seasonal Adjustment Program