  In time series analysis, traditional bootstrapping methods often fall short
due to their assumption of data independence, a condition rarely met in
time-dependent data. This paper introduces tsbootstrap, a python package
designed specifically to address this challenge. It offers a comprehensive
suite of bootstrapping techniques, including Block, Residual, and advanced
methods like Markov and Sieve Bootstraps, each tailored to respect the temporal
dependencies in time series data. This framework not only enhances the accuracy
of uncertainty estimation in time series analysis but also integrates
seamlessly with the existing python data science ecosystem, making it an
invaluable asset for researchers and practitioners in various fields.
