timefiller 0.1.2

A package for imputing missing data in time series

timefiller

timefiller is a Python package for time series imputation and forecasting. When applied to a set of correlated time series, each series is processed individually, leveraging correlations with the other series as well as its own auto-regressive patterns. The package is designed to be easy to use, even for non-experts.

Installation

You can get timefiller from PyPi:

pip install timefiller

But also from conda-forge:

conda install -c conda-forge timefiller

mamba install timefiller

Why this package?

While there are other Python packages for similar tasks, this one is lightweight with a straightforward and simple API. Currently, its speed may be a limitation for large datasets, but it can still be quite useful in many cases.

Basic Usage

The simplest usage example:

from timefiller import TimeSeriesImputer

df = load_your_dataset()
tsi = TimeSeriesImputer()
df_imputed = tsi(df)

Advanced Usage

from sklearn.linear_model import LassoCV
from timefiller import TimeSeriesImputer, PositiveOutput

df = load_your_dataset()
tsi = TimeSeriesImputer(estimator=LassoCV(), ar_lags=(1, 2, 3, 6, 24), multivariate_lags=6, preprocessing=PositiveOutput())
df_imputed = tsi(df, subset_cols=['col_1', 'col_17'], after='2024-06-14', n_nearest_features=35)

Check out the documentation for details on available options to customize your imputation.

Algorithmic Approach

timefiller relies heavily on scikit-learn for the learning process and uses optimask to create NaN-free train and predict matrices for the estimator.