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: Data Science
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
Randomized SVD with Power Iterations for Large Data Matrices
What is Randomized SVD? Few days ago, I happened to come across a question in a forum. Someone was asking for help about how to perform singular value decomposition (SVD) on an extremely large matrix. To sum up, the question was roughly something like following "I have a matrix of size 271520*225. I want to … Continue reading Randomized SVD with Power Iterations for Large Data Matrices
Shah Mahdi Hasan 