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所在平台: Udemy |
课程主页: https://www.udemy.com/course/learn-how-detect-dominant-cycles-spectrum-analysis/
课程评论：没有评论
课程名称：学习如何通过谱分析检测主导周期 课程概述：在几乎所有的周期分析平台中，谱模块都是核心组成部分。存在多种傅里叶变换的衍生应用，但哪种傅里叶应用最适合用于经济市场呢？本课程旨在提供答案，重点以通俗易懂的方式解释关键方面。课程主要内容包括：如何阅读谱图的基本原则，以及不同的傅里叶谱分析方法在检测精确周期长度（“频率”成分）上的性能比较。您将学习在金融市场中检测周期时的重要因素，并获得源代码以便自行使用。 课程还包括理解测量周期长度的基本计算，掌握正确的缩放、非整数插值转换不同单位（频率与时间）的方法，并学习如何阅读谱图。这些知识对于周期分析和相关预测的成功至关重要。了解这些内容将使您在自定义周期分析应用中更加成功。 进行金融市场周期分析时需要考虑多个问题。不幸的是，容易出现不正确的谱测量，导致错误的周期预测，无论是相位还是从谱图中获取的长度。本课程阐明了基于傅里叶的谱分析关键要素，您将学习为什么Goertzel算法在金融市场周期检测中优于经典的傅里叶变换。相较于快速傅里叶变换（FFT），Goertzel算法对于识别与金融市场相关的数据序列中的周期更简单且更高效。您将在本课程中理解这一点。
At the heart of almost every cycle analysis platform is a spectrum module.Various derivatives of the Fourier transform are available. But which application of Fourier is the "best" for use in economic markets? This course tries to provide an answer.Therefore, the course focuses on explaining the essential aspects in layman's terms:Fundamental aspects on "How to read a spectrum diagram" are at the center of the course. Different Fourier spectrum analysis methods are compared in terms of their performance in detecting exact cycle lengths ("frequency" components). Learn what is important in detecting cycles in the financial markets.Get the source code to implement for your own usageUnderstanding the basic calculations involved in measuring cycle length, knowing the correct scaling, correct non-integer interpolation, converting different units (frequency vs. time), and learning how to read spectral plots are all critical to the success of cycle analysis and related projection.Being equipped with this knowledge will allow you to have more success with your custom cycle analysis application.There are many issues to consider when analyzing and measuring cycles in financial markets. Unfortunately, it is easy to make incorrect spectral measurements resulting in inaccurate cycle projections either on wrong phase or length gathered from the spectrum plot.This course explains the key elements of a Fourier-based spectrum analysis. You will learn why the Goertzel algorithm outperforms classical Fourier transforms for the purpose of cycles detection in financial markets.Compared to an FFT, the Goertzel algorithm is simple and much more efficient for detecting cycles in data series related to financial markets. You will learn and understand why in this course.
