Time Series Analysis


  • From Default to Delightful: AI-Assisted Data Visualization

    I can never remember the exact syntax for visualization libraries. AI changes this. Now I describe what I want in natural language, iterate in conversation, and let the model handle the boilerplate. Five prompts transformed default charts into a visual story of WordPress’s 20-year evolution.

  • Investigating Seasonality in a Time Series: A Mystery in Three Parts

    Recently, I was asked to determine the extent to which seasonality influenced a particular time series. No problem, right? The statsmodels Python package has a seasonal_decompose function that seemed pretty handy; and there’s always Google! As it turns out, this was a bit trickier than I expected. In this post I’ll share some of the…

  • Time Series Analysis: When “Good Enough” is Good Enough

    Anomaly detection and time series forecasting are valuable in monitoring the financial and technical health of an organization. Proper modeling of time series requires accounting for periodic fluctuation; malicious users; data irregularity, saturation or scarcity; sudden peaks and drops. To account for these parameters, the modeler needs to select the proper model family, optimize the…