interpreTS.core.features package#

Submodules#

interpreTS.core.features.feature_length module#

interpreTS.core.features.feature_length.calculate_length(data)#

Calculate the number of data points in a time series.

Parameters#

datapd.Series or np.ndarray: The time series data for which the length feature is to be calculated.

Returns#

int: The number of data points in the provided time series.

Raises#

TypeError: If the data is not a valid time series type.
ValueError: If the data contains NaN values.

Examples#

>>> import pandas as pd
>>> data = pd.Series([1, 2, 3, 4, 5])
>>> calculate_length(data)
5

interpreTS.core.features.feature_mean module#

interpreTS.core.features.feature_mean.calculate_mean(data)#

Calculate the mean value of a time series.

Parameters#

datapd.Series or np.ndarray: The time series data for which the mean value is to be calculated.

Returns#

float: The mean value of the provided time series.

Raises#

TypeError: If the data is not a valid time series type.
ValueError: If the data contains NaN values.

Examples#

>>> import pandas as pd
>>> data = pd.Series([1, 2, 3, 4, 5])
>>> calculate_mean(data)
3.0

interpreTS.core.features.feature_peak module#

interpreTS.core.features.feature_peak.calculate_peak(data, start=None, end=None)#

Calculate the local maximum of a time series within an optional range.

Parameters#

datapd.Series or np.ndarray: The time series data for which the maximum value is to be calculated.
startint, str, or None, optional: The starting index, timestamp, or position for slicing the data. If None, the series starts from the beginning.
endint, str, or None, optional: The ending index, timestamp, or position for slicing the data. If None, the series ends at the last value.

Returns#

float: The local maximum of the specified range in the provided time series.

Raises#

TypeError: If the data is not a valid time series type.
ValueError: If the data contains NaN values.

Examples#

>>> import pandas as pd
>>> data = pd.Series([1, 2, 5, 4, 7])
>>> calculate_peak(data)
7.0
>>> calculate_peak(data, start=1, end=3)
5.0

interpreTS.core.features.feature_spikeness module#

interpreTS.core.features.feature_spikeness.calculate_spikeness(data)#

Calculate the spikeness (skewness) of a time series.

Parameters#

datapd.Series or np.ndarray: The time series data for which the spikeness is to be calculated.

Returns#

float: The spikeness (skewness) of the provided time series.

Raises#

TypeError: If the data is not a valid time series type.
ValueError: If the data contains NaN values.

Examples#

>>> import pandas as pd
>>> data = pd.Series([1, 2, 3, 4, 5])
>>> calculate_spikeness(data)
0.0

interpreTS.core.features.feature_std_1st_der module#

interpreTS.core.features.feature_std_1st_der.calculate_std_1st_der(data)#

Calculate the standard deviation of the first derivative of a time series.

Parameters#

datapd.Series or np.ndarray: The time series data for which the standard deviation of the first derivative is to be calculated.

Returns#

float: The standard deviation of the first derivative of the provided time series.

Raises#

TypeError: If the data is not a valid time series type.
ValueError: If the data contains NaN values.

Examples#

>>> import pandas as pd
>>> data = pd.Series([1, 2, 3, 4, 5])
>>> calculate_std_1st_derivative(data)
0.0

interpreTS.core.features.feature_trough module#

interpreTS.core.features.feature_trough.calculate_trough(data, start=None, end=None)#

Calculate the local minimum of a time series within an optional range.

Parameters#

datapd.Series or np.ndarray: The time series data for which the minimum value is to be calculated.
startint, str, or None, optional: The starting index, timestamp, or position for slicing the data. If None, the series starts from the beginning.
endint, str, or None, optional: The ending index, timestamp, or position for slicing the data. If None, the series ends at the last value.

Returns#

float: The local minimum of the specified range in the provided time series.

Raises#

TypeError: If the data is not a valid time series type.
ValueError: If the data contains NaN values.

Examples#

>>> import pandas as pd
>>> data = pd.Series([1, 2, 5, 4, 3])
>>> calculate_trough(data)
1.0
>>> calculate_trough(data, start=1, end=3)
2.0

interpreTS.core.features.feature_variance module#

interpreTS.core.features.feature_variance.calculate_variance(data, ddof=1)#

Calculate the variance value of a time series with specified degrees of freedom.

Parameters#

datapd.Series or np.ndarray: The time series data for which the variance is to be calculated.
ddofint, optional: Delta degrees of freedom. The divisor used in calculations is N - ddof, where N is the number of elements. A ddof of 1 provides the sample variance, and a ddof of 0 provides the population variance. Default is 1.

Returns#

float: The variance of the provided time series with specified degrees of freedom.

Raises#

TypeError: If the data is not a valid time series type.
ValueError: If the data contains NaN values.

Examples#

>>> import pandas as pd
>>> data = pd.Series([1, 2, 3, 4, 5])
>>> calculate_variance(data)
2.5
>>> calculate_variance(data, ddof=0)  # Population variance
2.0

interpreTS.core.features.histogram_dominant module#

interpreTS.core.features.histogram_dominant.calculate_dominant(data, bins=10, return_bin_center=False)#

Calculate the dominant value (mode) of a time series histogram.

Parameters#

datapd.Series or np.ndarray: The time series data for which the dominant value is to be calculated.
binsint, optional: The number of bins to use for creating the histogram, by default 10.

Returns#

float: The dominant value (mode) of the histogram.

Raises#

TypeError: If the data is not a valid time series type.
ValueError: If the data contains NaN values.

Examples#

>>> import pandas as pd
>>> data = pd.Series([1, 1, 2, 3, 3, 3, 4, 5])
>>> calculate_dominant(data)
3.0

interpreTS.core.features.seasonality_strength module#

interpreTS.core.features.seasonality_strength.calculate_seasonality_strength(data, period=2, max_lag=12)#

Calculate the strength of the seasonality in a time series based on autocorrelation.

Parameters#

datapd.Series or np.ndarray: The time series data for which the seasonality strength is to be calculated.
periodint, optional: The periodic interval to check for seasonality (default is 2).
max_lagint, optional: The maximum number of lags to consider for autocorrelation (default is 12).

Returns#

float: The seasonality strength, ranging from 0 to 1, where 1 indicates strong seasonality.

Raises#

TypeError: If the data is not a valid time series type.
ValueError: If the data contains NaN values or is too short to calculate seasonality.

Examples#

>>> import pandas as pd
>>> data = pd.Series([1, 2, 3, 2, 1, 2, 3, 2, 1, 2, 3, 2], index=pd.date_range("2023-01-01", periods=12, freq="M"))
>>> calculate_seasonality_strength(data, period=3)
0.75

interpreTS.core.features.trend_strength module#

interpreTS.core.features.trend_strength.calculate_trend_strength(data)#

Calculate the strength of the trend in a time series using linear regression.

Parameters#

datapd.Series or np.ndarray: The time series data for which the trend strength is to be calculated.

Returns#

float: The R-squared value representing the strength of the trend (0 to 1).

Raises#

TypeError: If the data is not a valid time series type.
ValueError: If the data contains NaN values.

Examples#

>>> import pandas as pd
>>> data = pd.Series([1, 2, 3, 4, 5])
>>> calculate_trend_strength(data)
1.0

interpreTS.core.features package#

Submodules#

interpreTS.core.features.feature_length module#

Parameters#

Returns#

Raises#

Examples#

interpreTS.core.features.feature_mean module#

Parameters#

Returns#

Raises#

Examples#

interpreTS.core.features.feature_peak module#

Parameters#

Returns#

Raises#

Examples#

interpreTS.core.features.feature_spikeness module#

Parameters#

Returns#

Raises#

Examples#

interpreTS.core.features.feature_std_1st_der module#

Parameters#

Returns#

Raises#

Examples#

interpreTS.core.features.feature_trough module#

Parameters#

Returns#

Raises#

Examples#

interpreTS.core.features.feature_variance module#

Parameters#

Returns#

Raises#

Examples#

interpreTS.core.features.histogram_dominant module#

Parameters#

Returns#

Raises#

Examples#

interpreTS.core.features.seasonality_strength module#

Parameters#

Returns#

Raises#

Examples#

interpreTS.core.features.trend_strength module#

Parameters#

Returns#

Raises#

Examples#

Module contents#