Beyond Market Intelligence/time series

time series

time series on Beyond Market Intelligence: a running collection of 6 stories we have gathered and hand-picked because they are worth your time. Every post here touches on time series in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around time series, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.

InfoQ

Presentation: Beyond Line Charts: Why Some Diversity in Telemetry Visualization Is Long Overdue

For years, system observability has relied too heavily on line charts, obscuring critical insights. Yao Yue, drawing on 15 years of experience operating large-scale systems, argues it's time for a change. This presentation, "Beyond Line Charts," explores the fundamental limitations of this default visualization and demonstrates how engineering leaders can transform telemetry data to directly address capacity, latency, and fleet-sizing challenges.

Machine Learning

Millwright — experimenting with an end-to-end machine learning framework in Rust [P]

Millwright is an open-source project exploring a complete machine learning workflow built in Rust, addressing gaps often found when integrating individual ML libraries. This framework streamlines the classical ML lifecycle—ingest, explore, preprocess, and beyond—by providing a common abstraction layer over existing Rust libraries and interoperating with the Python/ONNX ecosystem. Currently featuring capabilities like AutoML and drift monitoring, Millwright aims to provide a valuable execution layer across training, inference, and production.

Machine Learning

[R] SineKAN: Kolmogorov-Arnold Networks Using Sinusoidal Activation Functions

Introducing SineKAN: Kolmogorov-Arnold Networks leveraging sinusoidal activation functions—a compelling exploration of alternative activation strategies within KAN architectures. Initial investigations, detailed in a recent arXiv publication and peer-reviewed work (see links below), suggest promising results. While the concept isn't entirely novel, its relatively limited visibility warrants sharing for broader discussion. Discover the implementation and findings at the provided GitHub repository. For context on navigating the evolving data science landscape, consider "How to Shine as a Data Scientist in the Vibe Coding Era." Explore the research: [https://arxiv.org/abs/2407.04149](https://arxiv.org/abs/

Data Science

How do you debug a forecasting model today when the error is quite bad?

Encountering unexpectedly poor forecast performance? Diagnosing the root cause goes beyond a single error score. Experienced practitioners systematically investigate discrepancies, often breaking down errors by key dimensions like customer, product, or time horizon. Many routinely build custom notebooks and visualizations to facilitate this analysis—a significant manual effort. We’re exploring common workflows used to pinpoint issues, potentially informing an open-source tool for streamlined forecast evaluation. See "What to consider when creating waterfall charts" for a related perspective on data visualization techniques.

Data Science

Why Reddit Data Scientists Keep Saying Not To Use Prophet

A recurring sentiment within the Reddit data science community cautions against relying on Facebook’s Prophet for time series forecasting. This post explores why, presenting initial observations and a small experiment to understand the underlying concerns. While Prophet offers accessibility, the community often finds its limitations outweigh the benefits in more complex scenarios. For those seeking robust evaluation strategies to improve forecasting workflows, our article, "Structured Evaluation Pipelines to Improve Your AI Workflows," provides deeper insights.

Machine Learning

Recent project I worked on: End to End Edge ML platform [D]

Exciting progress in the tinyML space! A developer has released SensorForge, an end-to-end edge ML platform designed to streamline the journey from raw sensor data to deployed models on MCUs. This innovative platform addresses a key challenge: data labeling, featuring an auto-labeling tool specifically for time series sensor data. Additionally, SensorForge incorporates a chatbot for direct signal data analysis and insight generation. Explore this free and open-sourced project and contribute to its development; see the discussion surrounding NeurIPS 2026 AI-generated reviews for related insights. [https://sensorforge.dev/app](https://sensorforge.dev/app)