Empirical Mode Decomposition (EMD) is a powerful time-frequency analysis technique that allows for the decomposition of a non-stationary and non-linear signal into a series of intrinsic mode functions (IMFs). The method was first introduced by Huang et al. in 1998 and has since been widely used in various fields, such as signal processing, image analysis, and biomedical engineering.
Tag: time series
Time series data is widely used in various fields, such as finance, economics, and engineering. One of the key challenges when working with time series data is detecting level shifts. A level shift occurs when the time series’ mean and/or variance changes abruptly. These shifts can significantly impact the analysis and forecasting of the time series and must be detected and handled properly.
Time series machine learning techniques show great promise for the analysis of health care wearable data. As our busy lifestyles render continuous monitoring more and more essential, the need to analyze data to find correlations between these data streams becomes even more important, because they can provide important cues to
Macroeconometrics is not dead: (and I wish I had paid better attention in my time series course): I wrote this on the way to see one of our manufacturing clients in Austin, Texas, anticipating a discussion how to use vector autoregressive models in process control. It is a typical use
Gartner has stated that there are nearly five billion connected devices throughout the world today and predicts that there will be more than 25 billion by 2020, making the potential of this technology unlimited. The connected devices in industrial settings, in personal devices, and in our homes are creating a
The date of Easter influences our leisure activities Different from many other public holidays, Easter is a so-called movable holiday. This means that the Easter bunny brings more than just eggs for the statistician - he brings special Easter forecasting challenges. In the year 325 CE the Council for Nicea
You’ve heard about the smart grid, but what is it that makes the grid smart? I’ve been working on a project with Duke Energy and NC State University doing time-series analysis on data from Phasor Measurement Units (PMUs) that illustrates the intelligence in the grid as well as an interesting
This post will violate the “what happens in Vegas stays in Vegas” rule, because last week I had the pleasure of attending and participating in the Analytics 2014 event there and want to share some of what I heard for those who couldn’t attend. I was joined by over 1,000
Wavelet analysis is an exciting and relatively new field of study that enables one to extract underlying patterns either from spatially varying or temporally varying data. Pixel values representing the relative brightness and color that constitute an image are an example of spatially varying data, and daily variations of financial
Don’t worry! This is not an excerpt from a romantic love letter. The title of this blog post is an allusion to my talk on "Missing Values", at the A2013 conference in June in London. There is not much time for emotions: dealing with missing values in analysis is not