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Minimum of timeseries data


tsmin = min(ts)
tsmin = min(ts,Name,Value)



tsmin = min(ts) returns the minimum value of the data samples in a timeseries object.

tsmin = min(ts,Name,Value) specifies additional options when computing the minimum using one or more name-value pair arguments. For example, tsmin = min(ts,'Quality',-99,'MissingData','remove') defines -99 as the missing sample quality code, and removes the missing samples before computing the minimum.


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Create a timeseries object and compute the minimum data sample.

ts = timeseries((1:5)');
tsmin = min(ts)
tsmin = 1

Input Arguments

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Input timeseries, specified as a scalar.

Data Types: timeseries

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: tsmin = min(ts,'Quality',-99,'MissingData','remove')

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Missing value indicator, specified a scalar, vector, matrix, or multidimensional array of integers ranging from -128 to 127. Each element is a quality code to treat as missing data.

By default, min removes any missing data before computing the minimum. To interpolate the data instead of removing it, specify the name-value pair 'MissingData','interpolation'.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Missing data method, specified as either 'remove' to remove missing values before computing the minimum or 'interpolate' to fill missing values by interpolating the data. Specify the 'Quality' name-value pair to indicate which data samples are considered missing.

See Also

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Introduced before R2006a

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