Time Series Search (TSS)

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

 

.ai.tss.tss

.ai.tss.tss[ts;q;k;opts]

Conducts a TSS search with z-normalization and returns K nearest neighbors.

Parameters

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 > N (K set to N) and q longer than ts (returns empty). returnMatches (default false): if true, returns list of distances, indexes, and matched patterns. normalize (default true): if true, applies z-normalization to query and timeseries windows.

Returns

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

Example

 

.ai.tss.tssdist

.ai.tss.tssdist[ts;q;opts]

Conducts a full TSS distance matrix without z-normalization.

Parameters

Name	Type(s)	Description
ts	short[] | int[] | long[] | float[] | real[]	The data vector
q	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.

Returns

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

Example