Wavelet Based Time Series Models with Time-Varying Thresholds

TL;DR

This paper introduces a wavelet-based threshold model for time series that effectively captures irregular, abrupt, and smooth threshold variations, offering enhanced flexibility over traditional Fourier methods.

Contribution

The paper proposes a novel wavelet series expansion approach for time-varying thresholds in time series models, improving flexibility and adaptability.

Findings

Model captures abrupt and smooth threshold changes effectively.

Simulation and real-data tests demonstrate improved performance.

Wavelet approach outperforms Fourier-based methods in flexibility.

Abstract

This paper develops a threshold model with a time-varying threshold, represented using a wavelet series expansion. The model adequately captures irregular and abrupt variations, as well as smooth changes in the threshold parameter, allowing greater flexibility than Fourier-based approaches. Simulation experiments and real-data applications are used to evaluate the model's performance.