Nikolaos Kourentzes – Page 15 – Forecasting research

Improving forecasting by estimating time series structural components across multiple frequencies

N. Kourentzes, F. Petropoulos and J. R. Trapero, 2014, International Journal of Forecasting, 30: 291-302. http://dx.doi.org/10.1016/j.ijforecast.2013.09.006

On the identification of sales forecasting models in the presence of promotions

J. R. Trapero, N. Kourentzes and R. Fildes, 2014, Journal of the Operantional Research Society, 66: 299-307. http://dx.doi.org/10.1057/jors.2013.174

ANOM and Nemenyi tests

Code for the ANOM and Nemenyi tests for MatLab. Download here. For a discussion of the two tests and the various ways to visualise the results look at this post. Here are some examples, using the M3 results: >> anom(X,0.05,labels); The models in red are significantly better than the average (solid line). For the Nemenyi… Read More: ANOM and Nemenyi tests »

Statistical Significance of Forecasting Methods – an empirical evaluation of the robustness and interpretability of the MCB, ANOM and Friedman-Nemenyi Test

M. Hibon, S. F. Crone and N. Kourentzes, 2012, The 32nd Annual international Symposium on Forecasting, Boston.

Optimum parameters for Croston intermittent demand methods

N. Kourentzes, 2012, The 32nd Annual international Symposium on Forecasting, Boston.

Data Driven Fitting Sample Selection For Time Series Forecasting With Neural Networks

N. Kourentzes, 2012, International Joint Conference on Neural Networks, Brisbane, 10-15 June 2012.

Improving forecasting by estimating time series structural components across multiple frequencies

On the identification of sales forecasting models in the presence of promotions

ANOM and Nemenyi tests

Intermittent Demand Forecasts with Neural Networks

Analysis of judgmental adjustments in the presence of promotions

New Product Forecasting and Inventory Planning Using Time Series Clustering

Impact of Information Exchange on Supplier Forecasting Performance

Statistical Significance of Forecasting Methods – an empirical evaluation of the robustness and interpretability of the MCB, ANOM and Friedman-Nemenyi Test

Optimum parameters for Croston intermittent demand methods

Data Driven Fitting Sample Selection For Time Series Forecasting With Neural Networks