java--ml 时间序列

时间序列-进一步的范围 (Time Series - Further Scope)

Machine learning deals with various kinds of problems. In fact, almost all fields have a scope to be automatized or improved with the help of machine learning. A few such problems on which a great deal of work is being done are given below.

机器学习处理各种问题。 实际上，借助机器学习，几乎所有领域都有自动化或改进的范围。 下面给出了一些需要大量工作的问题。

时间序列数据 (Time Series Data)

This is the data which changes according to time, and hence time plays a crucial role in it, which we largely discussed in this tutorial.

这是随时间变化的数据，因此时间在其中起着至关重要的作用，我们在本教程中对此进行了大量讨论。

非时间序列数据 (Non-Time Series Data)

It is the data independent of time, and a major percentage of ML problems are on nontime series data. For simplicity, we shall categorize it further as −

它是与时间无关的数据，并且大部分ML问题都与非时间序列数据有关。 为了简单起见，我们将其进一步分类为-

• Numerical Data − Computers, unlike humans, only understand numbers, so all kinds of data ultimately is converted to numerical data for machine learning, for example, image data is converted to (r,b,g) values, characters are converted to ASCII codes or words are indexed to numbers, speech data is converted to mfcc files containing numerical data.

  数值数据 -计算机与人类不同，计算机只能理解数字，因此最终将各种数据转换为用于机器学习的数值数据，例如，将图像数据转换为(r，b，g)值，将字符转换为ASCII码或将单词索引为数字，则语音数据将转换为包含数字数据的mfcc文件。

• Image Data − Computer vision has revolutionized the world of computers, it has various application in the field of medicine, satellite imaging etc.

  图像数据 -计算机视觉彻底改变了计算机世界，在医学，卫星成像等领域具有多种应用。

• Text Data − Natural Language Processing (NLP) is used for text classification, paraphrase detection and language summarization. This is what makes Google and Facebook smart.

  文本数据 -自然语言处理(NLP)用于文本分类，释义检测和语言摘要。 这就是使Google和Facebook变得聪明的原因。

• Speech Data − Speech Processing involves speech recognition and sentiment understanding. It plays a crucial role in imparting computers the human-like qualities.

  语音数据 -语音处理涉及语音识别和情感理解。 它在赋予计算机类似人的品质方面起着至关重要的作用。

翻译自: https://www.tutorialspoint.com/time_series/time_series_further_scope.htm

java--ml 时间序列