1 views collected around this technical thread.
This article explains the concept of time series, describes its main patterns such as long‑term trend, seasonal, cyclical and random variations, and demonstrates a practical Python analysis of a three‑year sales dataset from a Taobao shop using pandas and matplotlib.
This article explains how to determine whether a time‑series dataset exhibits a monotonic trend using the non‑parametric Mann‑Kendall test, walks through its statistical foundations, shows the calculation steps with sample sales data, and provides a complete Python implementation for practical analysis.
The 2022 Open Source Big Data Heat Report, released at the Yunqi Conference, analyzes 102 active projects, discovers a 40‑month “Moore’s law” doubling of project heat, highlights three major trends—diversification, integration, and cloud‑native—and ranks the top 30 hottest open‑source big‑data projects.
This article explains the moving average method for time‑series analysis, covering simple, weighted, and trend moving averages, their formulas, appropriate use cases, selection of parameters, and how they help reveal long‑term trends by smoothing out periodic and irregular fluctuations.
The 2019 programming language trends report analyzes the adoption stages of languages such as Elixir, Rust, Swift, .NET Core, and emerging infrastructure DSLs like Ballerina and Pulumi, offering insights for technology leaders and developers on where to invest their learning and development resources.
The article presents the latest TIOBE programming language rankings, highlighting Haskell's position at 23, notable moves for MQL4, Hack, and Elixir, and provides visual trend charts for the top 10 languages along with a disclaimer about the data's scope and limitations.