spatial analysis and backup material
Query: resample_1sec. Code: etl_spikes
We analize the latency data depending on the position of the vehicle. We create a geohash per coordinate pair and calculate the average latency
latency per geohash
We see a similar pattern per modem upload
upload per geohash
The most interesting correlations with camera latency are on the spatial level
spatial correlation
We clearly see that incidents (camera_latency
> 400ms) are clustered in space
spatial distribution of incidents
We check the cell handover
handover cases spatially distributed
Handover is strangely highly correlated but not with camera latency
correlation between handover
Ongoing analysis
Code: stat_reample
We want to asset the performances using a LSTM starting with a baseline of a single layer
We first train a model with 16 fold cross validation and we than substitute each time some random value per feature. The performance of the model with a synthetic random feature should significantly drop for the most important predictors.
performance drop depending on the feature
We create rolling windows of the time series to see how we can cluster these windows into an essential dictionary of elements.
We start first with 18 clusters composed by series of 16 data points and we create some fundamental clusters
dictionary of time series windows
We make sure that the dimension of the cluster is pretty much orthogonal
orthogonality of dictionary