Correlation Analysis

TDgpt provides the following correlation analysis capabilities for time-series data.

CORR

CORR(expr)

CORR calculates the Pearson correlation coefficient between two time series, reflecting their linear correlation. For details on using the corr function, refer to the CORR function documentation.

:::note

The CORR function does not require TDgpt. You can use this function in TDengine TSDB directly.

:::

DTW

DTW uses a dynamic programming approach to perform nonlinear temporal alignment between two time series, then calculates their similarity. A smaller result indicates higher similarity.

The DTW function uses Manhattan distance (ignoring the time dimension). Euclidean distance is not supported.

Syntax

DTW(column1_name, column2_name, option_expr)

option_expr: {
"radius=expr
[,expr2]"
}

1. column1_name and column2_name: Two time series columns participating in DTW calculation.
2. option_expr: String specifying DTW parameters in comma-separated K=V format. Do not use quotation marks, escape characters, or non-ASCII characters.
3. radius=2 indicates a neighborhood radius of 2, limiting the DTW path to adjacent values within the distance matrix.
4. White noise detection is not supported, and algorithm selection is not required.
5. Maximum supported input is 10,240 rows. Exceeding this limit triggers Analysis failed since too many input rows (0x80000446).

Parameters

1. Supports numeric column input
2. Returns a double-precision floating-point value

Example

Calculate DTW between col1 and col2 without specifying radius:

SELECT dtw(col1, col2)
FROM foo;

Specify radius = 2:

SELECT dtw(col1, col2, 'radius=2')
FROM foo;

DTW_PATH

DTW_PATH uses dynamic programming to align two time series nonlinearly for similarity calculation. Unlike DTW, DTW_PATH returns the matching index pairs used in the similarity calculation.

Syntax

DTW_PATH(column1_name, column2_name, option_expr)

option_expr: {
"radius=expr
[,expr2]"
}

DTW_PATH has the same conditions and constraints as DTW but returns a string representing index mappings between the two series.

Example

taos> select col1, col2 from foo;
taos> select dtw_path(col1, col2,'radius=1') res from foo;

TLCC

TLCC returns correlation values between two time series under different time lags to evaluate dynamic relationships. This is commonly used to identify delayed effects between sequences and determine direction and magnitude.

Syntax

TLCC(column1_name, column2_name, option_expr)

option_expr: {
"lag_start=expr,
lag_end=expr,
[,expr2]"
}

1. column1_name and column2_name: Two time series columns used for analysis.
2. option_expr: String parameters in comma-separated key=value format. Do not use quotation marks or non-ASCII characters.
3. White noise detection is not supported, and algorithm selection is not required.
4. Maximum supported input is 10,240 rows; exceeding this triggers Analysis failed since too many input rows (0x80000446).

Parameters

1. Supports numeric columns only
2. Returns correlation values across different lag offsets

Example

Calculate correlation across default lag range:

SELECT tlcc(col1, col2)
FROM foo;

Specify lag range:

SELECT tlcc(col1, col2, 'lag_start=-10, lag_end=10')
FROM foo;