Forecasting is a common data science task that helps organizations with capacity planning, goal setting, and anomaly detection. Despite its importance, there are serious challenges associated with producing reliable and high quality forecasts - especially when there are a variety of time series and analysts with expertise in time series modeling are relatively rare. To address these challenges, we describe a practical approach to forecasting "at scale" that combines configurable models with analyst-in-the-loop performance analysis. We propose a modular regression model with interpretable parameters that can be intuitively adjusted by analysts with domain knowledge about the time series. We describe performance analyses to compare and evaluate forecasting procedures, and automatically flag forecasts for manual review and adjustment. Tools that help analysts to use their expertise most effectively enable reliable, practical forecasting of business time series.

chan算法（查恩）和Taylor（泰勒级数算法）的定位效果比较

用已有的4个基站的测距数据，分别用不同的算法(基于TOA的三边和最大似然，基于TDOA的Fang，Chan,Taylor，Friedland)计算出移动台的位置坐标。读者可以修改为自己的测距数据，实现未知节点的定位。

在不同数量基站下的CHAN算法、ＣＨＡＮ－Ｔａｙｌｏｒ算法的均方根误差对比，对算法都有中文注释，简单易懂

这是同作者的初级教程的继续——高级教程。用的人可能比较少，好不容易下下来。

对于做定位的人来说，是个基础并且很好的资源

三维定位chan_taylor，针对数学建模设计，matlab代码。简单修改参数可直接使用，注释清晰

多种定位算法合集

这是一个对于“A Simple and Efficient Estimator for Hyperbolic Location”这篇论文的实现以及拓展的项目，最终结果包括代码、图片、论文（latex）以及参考资料；项目通过实现传感器位置定位的算法进而比较验证文章算法的优越性，包括了2D情况下的定位算法、3D情况下的定位算法；以及比较了Si算法、泰勒算法、CR定理、Chan算法还有文章提出的优化算法的性能。

利用MATLAB 画taylor图 的程序包和实例~MATLAB 中没有taylor图的程序，但是taylor图对我们数据呈现非常有用。