Awesome

Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relation Discovery

ICDM 2017 - IEEE International Conference on Data Mining series (ICDM)

Conference Paper

Journal Extension

Commands for reproducing synthetic experiments:

Heat Diffusion

STNN

python train_stnn.py --dataset heat --outputdir output_heat --manualSeed 2021 --xp stnn

STNN-R(efine)

python train_stnn.py --dataset heat --outputdir output_heat --manualSeed 5718 --xp stnn_r --mode refine --patience 800 --l1_rel 1e-8

STNN-D(iscovery)

python train_stnn.py --dataset heat --outputdir output_heat --manualSeed 9690 --xp stnn_d --mode discover --patience 1000 --l1_rel 3e-6

Modulated Heat Diffusion

STNN

python train_stnn.py --dataset heat_m --outputdir output_heat_m --manualSeed 679 --xp stnn

STNN-R(efine)

python train_stnn.py --dataset heat_m --outputdir output_heat_m --manualSeed 3488 --xp stnn_r --mode refine --l1_rel 1e-5

STNN-D(iscovery)

python train_stnn_.py --dataset heat_m --outputdir output_m --xp test --manualSeed 7664 --mode discover --patience 500 --l1_rel 3e-6

Data format

The file heat.csv contains the raw temperature data. The 200 rows correspond to the 200 timestep, and the 41 columns are the 41 space points. The file heat_relations.csv contains the spatial relation between the 41 space points. It is a 41 by 41 adjacency matrix A, where A(i, j) = 1 means that series i is a direct neighbor of series j in space, and is 0 otherwise.