# Self Fulfilling Prophecy

In timeseries forecasting, the phenomenon that is most difficult to identify, yet the most dangerous to handle is "self fulfilling prophecy". Let me give you an example. Let's say you own a coffee shop. I give you a forecast that underestimates the demand for a given day. You take that forecast and schedule your baristas … Continue reading Self Fulfilling Prophecy

# 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

# My transition from MATLAB to Julia and learning from previous mistakes

I have been using MATLAB for eight good years. MATLAB served and still serves my purpose beautifully. In this entire span of time, I made repeated attempt to switch my go-to language from MATLAB to other alternatives, like Python or R, several times. The reason has never been that my work require something that is … Continue reading My transition from MATLAB to Julia and learning from previous mistakes

# Build the most powerful hypothesis test: Part 2

In the post Build the most power hypothesis test: Part 1 we introduced Neyman-Pearson criterion and Sufficient Statistic. In this post, using both of these ideas we will develop an optimum decision rule. To interactively generate likelihood functions and see the impact of choosing $latex \mathrm{P_{FA}}$, do not forget to try the Shiny app linked … Continue reading Build the most powerful hypothesis test: Part 2

# Build the most powerful hypothesis test: Part 1

I tried my best not to come up with such a click-bait-y title for this post. However, Mr. Neyman (1894-1981) and Mr. Pearson (1895-1980) didn't leave much room for that. This is Part-1 of this two-part post. I have built an interactive Shiny app in R for visualizing what goes under the hood of these … Continue reading Build the most powerful hypothesis test: Part 1

# Breaking down confusions over Fast Fourier Transform (FFT)

(This blog has already been appeared at my Medium and can be found here. Later, Analytics Vidhya published it under their publication as well.) Fourier Transform is undoubtedly one of the most valuable weapons you can have in your arsenal to attack a wide range of problems. FFT is literally the bread and butter for … Continue reading Breaking down confusions over Fast Fourier Transform (FFT)