Correlation

Note: We are using complaints.csv as an example here, with the following columns: date, complaints. The date column is used as the time index and complaints as the target variable.

The Correlation class provides methods for analyzing and visualizing autocorrelation (ACF) and partial autocorrelation (PACF) in univariate time series data. These tools are essential for understanding lag relationships and dependencies in time series analysis.

Features

Plots autocorrelation (ACF) for a given time series and number of lags.
Plots partial autocorrelation (PACF) for a given time series and number of lags.
Supports both instance-based and standalone usage.
Optionally logs plots to HTML reports.

Class: `Correlation`

Initialization

Correlation(df: pd.DataFrame = None, target_col: str = None, lags: int = 20, output_filepath: str = None)

df: The time series DataFrame (indexed by the time column).
target_col: The column name of the univariate time series to analyze.
lags: Number of lags to use for correlation plots (default: 20).
output_filepath: Path prefix for saving HTML reports and plots.

Note: Your DataFrame should have a time-based index (e.g., "date", "timestamp").

Methods

`acf_plot(data: pd.Series = None, lags: int = None, save: bool = True, output_filepath: str = None)`

Plots the autocorrelation function (ACF) for the specified time series and number of lags.

data: Optional. A pandas Series to plot. If not provided, uses the instance's DataFrame and target column.
lags: Optional. Number of lags to plot. Defaults to the instance's lags.
save: Optional. Whether to save the plot to an HTML report.
output_filepath: Optional. Path for saving the report.

Standalone Example:

from dynamicts.lag_correlation import Correlation

# Instance-based usage
corr = Correlation(df, target_col="complaints", lags=30, output_filepath="report")
fig = corr.acf_plot()
fig.show()

# Standalone usage
corr = Correlation()
fig = corr.acf_plot(data=df["complaints"], lags=30, output_filepath="report")
fig.show()

`pacf_plot(data: pd.Series = None, lags: int = None, save: bool = True, output_filepath: str = None)`

Plots the partial autocorrelation function (PACF) for the specified time series and number of lags.

data: Optional. A pandas Series to plot. If not provided, uses the instance's DataFrame and target column.
lags: Optional. Number of lags to plot. Defaults to the instance's lags.
save: Optional. Whether to save the plot to an HTML report.
output_filepath: Optional. Path for saving the report.

Standalone Example:

from dynamicts.lag_correlation import Correlation

# Instance-based usage
corr = Correlation(df, target_col="complaints", lags=30, output_filepath="report")
fig = corr.pacf_plot()
fig.show()

# Standalone usage
corr = Correlation()
fig = corr.pacf_plot(data=df["complaints"], lags=30, output_filepath="report")
fig.show()

Notes

The DataFrame should be indexed by the time column for proper time series analysis.
Plots can be logged to HTML reports if save=True and output_filepath is provided.