This is my implementation of the well known algorithm for traffic forecast; the Latent Space Model for Road Networks (LSMRN) using Matrix Profile algortihm in with stumpy in Python .

Here I try to adapt LSMRN to work with time series in general.

I assume the time series being a weighted (planar) path graph. Where the weights between two vertices is the time series value.

The idea is to use spatial information among the time series learned by another data-driven learning method as the Matrix Profile or other correlation methods to define some kind of distances between the nodes.

To achieve that I use first the original k-hop similarity matrix and the Matrix Profile algorithm.

The code has five main parts structured as follow:

1. Preprocess

   • load data
   • split in train and test
   • as negative values are not supported, we shift the train dataset.
   • split data in a list of snapshots, eg: [0:99], [100,199], ...

2. Adjacency

   • generate adjacency matrices G_1, G_2, ... from the snapshots.
   • create corresponding Y_t indication matrices.

3. Proximity matrix and some constants

   • compute matrix profile for each snapshot and combine them in one general proximity matrix.
   • normalize the proximity matrix.

4. Global learning

   • U_t computation
   • B computation
   • A computation

5. Forecasting

   • see output adjacency matrix as planar graph
   • some metrics

6. plots