ml.timeseries.timeops

module ml.timeseries.timeops

The timeops module provides helpful functionality for timeseries datasets

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function set_timeseries

set_timeseries(df: DataFrame, time_column: str) → DataFrame

Transforms the dataframe to a timeseries enabled dataframe by applying a DateTimeIndex to the given column

Args:

• `df` (pd.DataFrame): The DataFrame that should be time-indexed

Returns:

• `pd.DataFrame`: The DataFrame, including the DatetimeIndex

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function add_time_reference

add_time_reference(    sorted_df: DataFrame,    n: int,    reference_column: str,    new_column: str) → DataFrame

This method will add a reference column to the DataFrame that contains the value of reference column of n items before

Args:

• `sorted_df` (pd.DataFrame): a DataFrame that is sorted by the time
• `n` (int): the number of records to look back for the referencing columns
• `new_column` (str): the name of the new column

Returns:

• `pd.DataFrame`: the dataframe with the new column created

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function time_slice

time_slice(    df: DataFrame,    time_column: str,    start: datetime = None,    end: datetime = None)

This method takes a time series DataFrame and only returns the time slice, based on the start & end date

Args:

• `df` (pd.DataFrame): The indexed Data Frame , which should be a DatetimeIndex
• `start` (datetime): The start time of the time slice. When skipped, the time slice begins at the beginning of the index
• `end` (datetime): The end time of the time slice. When skipped, the time slice end at the end of the index

Returns:

• `pd.DataFrame`: the dataframe only containing the records inside the time slice

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function get_windows

get_windows(    sorted_df: DataFrame,    window_size: int,    window_stride: int = 1,    group_column: str = None,    zero_padding: bool = False,    remove_group_column: bool = False,    target_column: str = None) → <built-in function array>

This method take a DataFrame and returns a set of time windows of a specific length and a given column, eventually grouped by another column

Args:

• `sorted_df` (pd.DataFrame): A sorted Data Frame by a DatetimeIndex, that contains all time values
• `window_size` (int): The size of a window. How much record values should be added in every window. Consider this as a slice of the time series.
• `window_stride` (int): How much records should be between the different windows? (Default: 1)
• `group_column` (str): The name of the column on which you should group the time windows. This could be something like device_id. Optional.
• `zero_padding` (bool): If True, zeros will be added in the first time windows, to fill the array, prior to the first values.
• `remove_group_column` (bool): Indicates if the actual group column should be removed from the destination data frame
• `target_column` (str): Used to return a related array of values, taking from the column with this name. Commonly used to specify classes in training sets.

Returns: a tuple with the following objects:

• `np.array`: A multi dimensional array with all the windows, eventuall grouped
• `np.array`: An array, with all the linked target values

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function combine_time_ranges

combine_time_ranges(*args: DataFrame)

This method combines multiple timeseries (as DataFrame) and removes the overlapping time sections

Args:

• `*args (pd.DataFrame)`: A list of DataFrames, containing time series and the same layout.

Returns:

• `pd.DataFrame`: A DataFrame, containing all unique, ordered time series data from the given DataFrames

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