Hi John, the wiki is smoking for me at the moment so I haven't been able to update any notes.

So the general dimension reduction is going to depend a bit on what you're going to do with it. My thought is that looking at correlation between different spatial and temporal anomalies is a quite promising "primitive", so I've been thinking about making a feature vector/matrix out of:

1. An ordered pair of spatial "locations", so that's 30 * 29 possibilitites

2. Temporal lag for the first location: 0 days, 32 days, 64 days, 128 days

3. Temporal lag for second location relative to first: 0 days, 32 days, 64 days, 128 days.

4. Correlation window length: 128 days, 256 days

5. Whether we're using the maximum or minimum correlation value. (Later could refine this to say 5% & 95% quartile value to reduce ouliers effect.)

Points 2--5 are quite coarsely grained but give 4 * 4 * 2 * 2 = 64 choices. This gives an 870 * 64 matrix (or possibly flattened into a vector) of features at each time point, which is about the sort of size that my laptops can handle. If it proves in any way promising we can look at trying to do bigger, less coarse computations on bigger machines.

So that's what I'm thinking about trying to achieve.

(Ctd)

So the general dimension reduction is going to depend a bit on what you're going to do with it. My thought is that looking at correlation between different spatial and temporal anomalies is a quite promising "primitive", so I've been thinking about making a feature vector/matrix out of:

1. An ordered pair of spatial "locations", so that's 30 * 29 possibilitites

2. Temporal lag for the first location: 0 days, 32 days, 64 days, 128 days

3. Temporal lag for second location relative to first: 0 days, 32 days, 64 days, 128 days.

4. Correlation window length: 128 days, 256 days

5. Whether we're using the maximum or minimum correlation value. (Later could refine this to say 5% & 95% quartile value to reduce ouliers effect.)

Points 2--5 are quite coarsely grained but give 4 * 4 * 2 * 2 = 64 choices. This gives an 870 * 64 matrix (or possibly flattened into a vector) of features at each time point, which is about the sort of size that my laptops can handle. If it proves in any way promising we can look at trying to do bigger, less coarse computations on bigger machines.

So that's what I'm thinking about trying to achieve.

(Ctd)