Couple of weeks ago, I was hunting for a non-linear association measure for a use case I was working on. That’s when I came across this paper that introduces "Rearrangement Correlation." It provides a fresh take on the tried-and-tested Pearson's r. I couldn't use the result for my specific problem. Nonetheless, the paper is a pretty … Continue reading Rearrangement Correlation: First Principle Thinking to capture Non-linear Relationships?
Tag: Machine Learning
Vapnik’s Principle: What was actually said?
If you google Vapnik's Principle, this is the first search result: When solving a problem of interest, do not solve a more general problem as an intermediate step We will come back to this "principle" later. Allow me to take a rather elaborate detour for now. Over the years, I've delved into several self-help books. … Continue reading Vapnik’s Principle: What was actually said?
Finally a good start for the foundational models for timeseries?
Plot twist: It is Chronos by AWS Supply Chain Optimization Technology (SCOT). NeurIPS 2023 saw a proliferation of papers on the applicability of LLMs in time series forecasting. Some of the papers were so bad that I seriously have started (and continue to do so) questioning the review process of NeurIPS. Seeing that particular trend, … Continue reading Finally a good start for the foundational models for timeseries?
Avoiding Data Leakage in Timeseries 101
You've Already Made The Choice. You're Here To Understand Why You've Made It.The Oracle, The Matrix Reloaded Timeseries is one of the very few data disciplines where things are getting difficult to model, almost every day. For example, the abundance of data is a great news for many other domains. We can train better model … Continue reading Avoiding Data Leakage in Timeseries 101
Shah Mahdi Hasan 