Adaptive Piecewise Constant Approximation (APCA)

Applies the APCA algorithm to split the input time series into a number of constant-valued pieces of varying length while minimizing the mean-square error. It outputs a table containing indices for slices with meta values(e.g. errors) as table attributes. The second output contains approximated values and column attributes with per-column error

Documentation

The algorithm uses haar-transforms and heuristics for generating the segments, meaning that a global optima is not guaranteed

Definition

Input ports

input table

input

Output ports

output indices table

output indices

output values table

output values

Configuration

Max error (max_error)

If non-zero then increase number of segments until error is less than this. Due to heuristic functions error may be overshoot slightly

Number of segments (n_segments)

Number of segments to generate

Select master column (split column)

The column on which the APCA algorithm is run, all other columns will be split using the same segments as those generated for the master column

Select time column (time column)

The time column is passed through without modification

Examples

• apca.syx

Implementation

class node_apca.APCATransform