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Last Updated: 4th July 2016

Repository for the MATLAB code for forecast error metric customization paper. Full-text available for 50 days from publication from here: http://authors.elsevier.com/a/1TpCf,rUrFQTNo, DOI link (available permanently but sub-scription required): http://dx.doi.org/10.1016/j.est.2016.09.005

All of the main files are in the folder ./mainScripts which contains the following scripts:

  1. Config.m This is a function which takes pwd as an argument, and sets up a cfg structure which controls settings of the optimization problem and the training of forecasts (and sets filenames for saving etc.)

  2. compareForecasts.m This runs training of multiple forecasts, and then plots the performance of the various methods as measured using the various metrics. This script produces the results plotted in Figures 4, 5 and 6 of the paper. Please note that for this script to work you must have R installed on your computer, and with your eviornment path set up so it can be run from the command line / terminal. It is also necessary to have a number of additional R libraries installed (with intial Config.m values this takes approximately 9hrs to run on a Intel i7-4770 3.4GHz processor).

  3. metricSelectMain.m Trains multiple forecasts (including multiple parameter values of PFEM and PEMD forecasts), tests these forecasts on a parameter selection dataset, chooses the parameter values for PFEM, PEMD forecasts, and then does a final test on an unseen test dataset. Used in producing Figure 7 in the paper (with intial Config.m values this takes approximately 6hrs to run on a Intel i7-4770 3.4GHz processor).

  4. compileMexes.m This compiles the MEX files (required if running simulations on another platform).

  5. LoadFunctions.m This loads all of the required functions/scripts into the matlab PATH so that they can be called.

  6. runAllUnitTests.m This runs a series of unit tests on the various functions/utilities to confirm everything is installed correctly. It should take about 10 minutes or so to run on a modern desktop (Intel CORE i7). Again this script requires R to be installed and have the relevant libraries loaded; if this is not the case the tests of the R forecasting scripts, test_getAutomatedForecastR_simple, test_getAutomatedForecastR_onDemand and test_forecastSarma can be commented out.

  7. There are three additional functions to help facilitate parralelizing, metSelMainNcust.m is a function which just packages the metricSelectMain.m script, so that it can be called more easily on different problem instances. recombineResults.m allows plotting and saving files from multiple metricSelectMain.m runs. metricSelectMain_no_forecast.m runs a version of the metricSelectMain.m script, in which existing forecast models are loaded, rather than generated from historic data. (runRecombineResults.m is just a script which facilitates calling recombineResults.m which specific filenames).

In addition to these main scripts there are many functions in the folder ./functions and its subfolders; which are called from these scripts.

Whilst all the results presented in the paper are using FFNN forecasts for the parameterized forecasts, more recent testing has shown that this approach is effective even when using the 4-parameter SARMA forecasts.

Note that the online repository does not include the data, partly because this is quite large 2.6GB in txt file format, and partly because access to this data needs to be requested from ISSDA. Requesting access is straight-forward, and is done by submitting a form via e-mail, details here. Assuming nothing has changed since I accessed it, this data will be provided as 6 compressed text files. If they are labelled File1.txt to File6.txt, and placed in the ./data/ folder, the running ./data/importIssdaDataAll.m should create a matlab data-file in the format and location needed to be called by the other scripts (this takes approximately 1hr to run on a Intel i7-4770 3.4GHz processor, and requires significant free RAM (>4GB)).

Because of the large number of forecasts which need to be trained, and the time-consuming process of training neural-network forecasts, especially for loss functions which don't have analytical gradients, running compareForecasts.m and metricSelectMain.m is time-consuming, and probably best done on a compute cluster. The code is configured to run different problem instances (aggregations of customers) in parrallel, the parameter cfg.sim.nProc set in Config.m sets the number of processors available.