# Import to be able to import python package from src
import sys
sys.path.insert(0, '../src')

import pandas as pd
import numpy as np
import ontime as on

The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md
The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md

Time Series

a TimeSeries is the basic object in onTime. It is basically an extended version of Darts TimeSeries. Therefore, it is multivariate by default and all Darts methods can be used.

Get some data

Let’s generate a random walk time series

ts = on.generators.random_walk().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31'))

The TimeSeries can be sliced and its data structure is an xarray.

ts[0:5]

<TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>
array([[[-0.36981256]],

       [[-1.89552543]],

       [[-3.58416814]],

       [[-4.19590597]],

       [[-3.08642833]]])
Coordinates:
  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-01-05
  * component  (component) object 'random_walk'
Dimensions without coordinates: sample
Attributes:
    static_covariates:  None
    hierarchy:          None

TimeSeries (DataArray)

• time: 5
• component: 1
• sample: 1

• -0.3698 -1.896 -3.584 -4.196 -3.086

  array([[[-0.36981256]],
  
         [[-1.89552543]],
  
         [[-3.58416814]],
  
         [[-4.19590597]],
  
         [[-3.08642833]]])

• Coordinates: (2)
  • time
    (time)
    datetime64[ns]
    2022-01-01 ... 2022-01-05

    array(['2022-01-01T00:00:00.000000000', '2022-01-02T00:00:00.000000000',
           '2022-01-03T00:00:00.000000000', '2022-01-04T00:00:00.000000000',
           '2022-01-05T00:00:00.000000000'], dtype='datetime64[ns]')

  • component
    (component)
    object
    'random_walk'

    array(['random_walk'], dtype=object)

• Indexes: (2)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2022-01-01', '2022-01-02', '2022-01-03', '2022-01-04',
                   '2022-01-05'],
                  dtype='datetime64[ns]', name='time', freq='D'))

  • component
    PandasIndex

    PandasIndex(Index(['random_walk'], dtype='object', name='component'))

• Attributes: (2)

  static_covariates :

  None

  hierarchy :

  None

Plot

As expected, you can call the plot() methdod

ts.plot();