Documentation

# min

Minimum of `timeseries` data

## Syntax

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

## Description

example

````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.```

## Examples

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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 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')```

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.

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