Post Hoc Explainability for Time Series Classification: Toward a signal processing perspective

Time-Frequency Signal Processing: A Statistical Perspective

Time-frequency methods are capable of analyzing and/or processing nonstationary signals and systems in an intuitively appealing and physically meaningful manner. This tutorial paper presents an overview of some time-frequency methods for the analysis and processing of nonstationary random signals, with emphasis placed on time-varying power spectra and techniques for signal estimation and detect...

a time-series analysis of the demand for life insurance in iran

با توجه به تجزیه و تحلیل داده ها ما دریافتیم که سطح درامد و تعداد نمایندگیها باتقاضای بیمه عمر رابطه مستقیم دارند و نرخ بهره و بار تکفل با تقاضای بیمه عمر رابطه عکس دارند

Signal processing and time series description: A Perspective of Computational Intelligence and Granular Computing

This study provides a general introduction to the principles, algorithms and practice of Computational Intelligence (CI) and elaborates on their use to signal processing and time series. In this setting, we discuss the main technologies of Computational Intelligence (namely, neural networks, fuzzy sets or Granular Computing, and evolutionary optimization), identify their focal points and stress...

Signal processing and neural network models for economic time series

Evolutionary Signal Processing Senior Project Report Financial Time Series Predictions

Assume there exists information in past data that relates to future trends. This is the case with our physical world. It allows physics models to predict exactly how fast an apple will fall. With historical data in the physical world, such as the apple has been 8m above the surface for a long time and that the apple's stem breaks at t = 0, it is possible to predict where the apple will be in th...