Time Series Search (TSS)

This page describes the Time Series Search (TSS) parameters as part of AI libs.

.ai.tss.tss

The .ai.tss.tss function conducts a TSS search with z-normalization and returns k nearest neighbors.

Name	Type(s)	Description
ts	short[] \| int[] \| long[] \| float[] \| real[]	The data vector
q	short[] \| int[] \| long[] \| float[] \| real[]	The query vector
k	short \| long \| int	The number of nearest neighbors to return
opts	dict	Advanced options (optional): `ignoreErrors` (default `false`): if `true`, allows `k` > `count[ts]` (`k` set to `count[ts]`) and `q` longer than `ts` (returns empty). `returnMatches` (default `false`): if `true`, returns a list of distances, indexes, and matched patterns. `normalize` (default `true`): if `true`, applies z-normalization to query and timeseries windows.

Type	Description
(float[]; long[])	List of distances and indexes

Copy

q)\l ai-libs/init.q
q).ai.tss.tss[10?1f;3?1f;5;::]

0.9182925 0.9312366 1.44556 2.310831 2.842262
2         6         1       4        7

The .ai.tss.tssdist function creates a list of sliding z-normalized distances.

Name

Type(s)

Description

short[] | int[] | long[] | float[] | real[]

The data vector

short[] | int[] | long[] | float[] | real[]

The query vector

opts

dict

Advanced options (optional):

normalize (default true): if true, applies z-normalization to query and timeseries windows.

Type	Description
float[]	The TSS distances among all possible positions

Copy

q)\l ai-libs/init.q
q).ai.tss.tssdist[10?1f;3?1f;::]

0.1624568 0.6407308 0.9527249 0.8994361 0.6351341 0.8521289 0.9975543 0.8938303