Struct TimeSeries 

pub struct TimeSeries {
    pub name: String,
    pub timestamps: Vec<i64>,
    pub values: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>,
    pub frequency: Option<Frequency>,
    pub metadata: HashMap<String, String>,
}

Time series data structure

Fields

name: String

Series name

timestamps: Vec<i64>

Time index (Unix timestamps in seconds)

values: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>

Values

frequency: Option<Frequency>

Frequency (if regular)

metadata: HashMap<String, String>

Metadata

Implementations

impl TimeSeries

pub fn new( name: impl Into<String>, timestamps: Vec<i64>, values: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>, frequency: Option<Frequency>, ) -> Result<TimeSeries, Error>

Create a new time series from arrays

pub fn from_dataframe( df: &DataFrame, value_col: &str, date_col: &str, ) -> Result<TimeSeries, Error>

Create from DataFrame with date and value columns

pub fn regular( name: impl Into<String>, values: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>, frequency: Frequency, ) -> TimeSeries

Create a regular time series with integer index

pub fn len(&self) -> usize

Get series length

pub fn is_empty(&self) -> bool

Check if series is empty

pub fn name(&self) -> &str

Get series name

pub fn values(&self) -> &ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>

Get values

pub fn timestamps(&self) -> &[i64]

Get timestamps

pub fn frequency(&self) -> Option<Frequency>

Get frequency

pub fn set_metadata(&mut self, key: impl Into<String>, value: impl Into<String>)

Set metadata

pub fn get_metadata(&self, key: &str) -> Option<&String>

Get metadata

pub fn start_time(&self) -> Option<i64>

Get start time

pub fn end_time(&self) -> Option<i64>

Get end time

pub fn stats(&self) -> TimeSeriesStats

Compute basic statistics

pub fn diff(&self, lag: usize, order: usize) -> Result<TimeSeries, Error>

Differencing: y_t - y_{t-d}

pub fn log(&self, offset: f64) -> TimeSeries

Log transformation (with offset to handle zeros/negatives)

pub fn boxcox(&self, lambda: f64) -> TimeSeries

Box-Cox transformation

pub fn slice( &self, start: Option<i64>, end: Option<i64>, ) -> Result<TimeSeries, Error>

Slice time series

pub fn fillna(&self, method: FillMethod) -> TimeSeries

Fill missing values (NaN) using specified method

pub fn detect_outliers(&self, threshold: f64) -> Vec<usize>

Detect outliers using IQR method

pub fn to_dataframe(&self) -> DataFrame

Convert to DataFrame

Trait Implementations

impl ARIMAExt for TimeSeries

fn arima(&self, p: usize, d: usize, q: usize) -> Result<ARIMAResults, Error>

Fit ARIMA model

impl Clone for TimeSeries

fn clone(&self) -> TimeSeries

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for TimeSeries

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl DecompositionExt for TimeSeries

fn decompose_ma( &self, method: DecompositionMethod, period: usize, ) -> Result<DecompositionResults, Error>

Moving average decomposition

fn decompose_stl(&self, period: usize) -> Result<DecompositionResults, Error>

STL decomposition

fn hp_filter(&self, lambda: f64) -> Result<(TimeSeries, TimeSeries), Error>

Hodrick-Prescott filter

fn x12_adjust(&self, period: usize) -> Result<TimeSeries, Error>

X-12-ARIMA seasonal adjustment

impl<'de> Deserialize<'de> for TimeSeries

fn deserialize<__D>( __deserializer: __D, ) -> Result<TimeSeries, <__D as Deserializer<'de>>::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl GARCHExt for TimeSeries

fn garch(&self, p: usize, q: usize) -> Result<GARCHResults, Error>

Fit GARCH model

fn arch(&self, q: usize) -> Result<GARCHResults, Error>

Fit ARCH model

impl Serialize for TimeSeries

fn serialize<__S>( &self, __serializer: __S, ) -> Result<<__S as Serializer>::Ok, <__S as Serializer>::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.