- System requirements
a. The codes can be run within MATLAB environment on any operating system.
b. We implemented the codes with MATLAB R2019b on Mac/Unbuntu.
c. No non-standard hardware is required.
- Installation guide
a. The codes can be run directly without installation.
b. No install time is needed.
- Demo:
The code "Main codes/LongerPredictionSamples_ARNN.m" in repository can generates the results in Figure 2d,2e,2f of the main text.
Expected running time for this demo is less than 1 minute on a "normal" desktop computer.
- Instructions for use
Run ARNN algorithm:
Resource code file folder: Main codes
Use "Main codes/Main_ARNN.m" for both Lorenz model and real-world datasets.
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For Lorenz model simulation, there are the following three cases:
noise-free & time-invariant case: use "Main codes/mylorenz.m" to generate high-dimensional data, set ""noisestrength = 0" in "Main codes/Main_ARNN.m";
noisy & time-invariant case: use "Main codes/mylorenz.m" to generate high-dimensional data, set "noisestrength" to be 0.1-1.0 in "Main codes/Main_ARNN.m", respectively;
time-varying case: use "Main codes/mylorenz_dynamic.m" to generate high-dimensional data, set "noisestrength = 0" in "Main codes/Main_ARNN.m".
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For windspeed dataset, unzip the compressed files first, then
cat scale_windspeed_PARTa* > scale_windspeed_a.txt
M = dlmread('scale_windspeed_a.txt');
save('scale_windspeed_a.mat', M);
load wind speed data: "load scaled_windspeed_a" in Matlab;
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For high-dimensional datasets, use "Main codes/calcv.m" for variable selection by mutual information or PCC.
Test the ARNN Robustness:
File folder: Robustness test
Prediction results:
"Lorenz results",
Movie1: typhoon prediction.mp4,
Movie2: traffic prediction.mp4
Data resources:
Folder: Data, which includes gene expression, HK hospital admission, Ozone(tempreture and SLP), Solar, wind speed, stock, traffic, typhoon dataset.