by Jonathan Widarsa

by Jonathan Widarsa

on the theory and practice of unveiling structure behind data.


  • Reasoning a World We Cannot See

    Reasoning a World We Cannot See

    My fiancée has this supernatural ability she calls gut feeling where she’s able to somewhat accurately able to sense a hidden truth. The other day, she told me out of the blue that she felt a little nauseous, and then out of the blue, that perhaps so-and-so we’re broken up. Then we’d stalk their socials […]

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  • The More Realistic Fourier Transform

    The More Realistic Fourier Transform

    Previously, we’ve taken a look at the continuous Fourier transform (FT), which is a powerful tool for decomposing a signal into its constituent frequencies. However, as we’ve briefly mentioned in the conclusion of that article, in practice, we never actually observe a continuous signal. Therefore, the tool is useless and we end our discussion here. […]

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  • Series Have Friends Too

    Series Have Friends Too

    In my previous post, we delved quite deep into time series models like AR, MA, ARMA, and ARIMA. Essentially, by capturing different aspects of a series’ memory, these models usually effectively extract autocorrelation out of data into their structural parameters. I actually have to apologize—to simplify definitions, I intentionally omitted an important label: univariate. These […]

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  • The Progressive Ace Game

    The Progressive Ace Game

    As a brief break from my usual, more rigorous content, I thought it’d be fun to explore some games. Of course, to stay consistent with the themes of my blogs, these games will still be rooted in statistics. As we’ll soon see, the puzzles revolve around how uncertainty behaves and how small changes can dramatically […]

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  • AR MA ARMA ARIMA!

    AR MA ARMA ARIMA!

    One of my favorite topics in time series analysis is forecasting, which is the art of modeling memory. What’s memory? In a previous post, we explored this concept in depth through the lens of autocorrelation. On top of understanding how it’s measured, we established that it’s basically a non-negotiable heartbeat for any time series dataset. Now, if autocorrelation […]

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  • Making Waves with Fourier

    Making Waves with Fourier

    In the context of time series analysis, as statisticians, we’re more than comfortable with thinking about data in the time domain. Here, we have values which evolve across time, and the questions we’re interested in often follow this perspective: Is the series trending? Is it noisy? Is today’s data dependent on yesterday’s? This view is […]

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