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@avikumart_

  • Will Large Models Replace Traditional Machine Learning for Tabular Data?

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    @avikumart_

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    There's one question that persists: will conventional ML still remain relevant, given the advancement of large models, which can very much analyse the tabular data and make models out of it? There can be multiple arguments here that large models may outperform, or it might be just not good enough. But the major issue would be actual deployment in real world system where they need to work with tabular data and generate the outcomes as fast as possible. if it can do then traditional ml can be bypassed easily imo

    → View original post on X — @avikumart_, 2026-03-04 04:33 UTC

  • Modern Time Series Analysis with R: ARIMA, Tidyverse, and Causal Inference

    By

    @avikumart_

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    I’ve recently been diving into Modern Time Series Analysis with R, and it has been a game-changer for how I approach sequential data. As someone deeply invested in the intersection of Machine Learning and Data Science, seeing a structured bridge between traditional statistical theory and modern computational techniques is incredibly refreshing. Why this book stands out for me: 1. The "Arima" of it all: Understanding the Logic What I found most fascinating was the deep dive into ARIMA (AutoRegressive Integrated Moving Average) models. While we often jump straight to LSTMs or Transformers in deep learning, this book reinforces why ARIMA remains a powerhouse for business applications. The way it breaks down: Autoregression (p): Using past values to predict the future. Integration (d): Differencing data to achieve stationarity—a crucial step I’ve applied in my own research. Moving Average (q): Modeling the error term as a linear combination of past errors. Understanding these components isn't just about math; it’s about understanding the "memory" of the data. 2. Structured Learning with R & Tidyverse The book doesn’t just throw code at you; it teaches a structured workflow. Using the tidyverse and specific time-series wrappers makes data wrangling—which is usually 80% of the work—feel intuitive. From handling hierarchical models to automating reproducible reports in RStudio, the focus is on building a pipeline that is production-ready. 3. Beyond Simple Forecasting It was eye-opening to see time series applied to Causal Inference and Change Point Analysis. In complex domains like healthcare or finance, knowing when a structural change occurred is often more valuable than just predicting the next data point. If you are someone who's looking to deep dive into time series modelling using R, then this book is just right for you! Link: lnkd.in/gfRxu7zp

    → View original post on X — @avikumart_, 2026-03-03 21:19 UTC