Time Series 简明教程

Time Series - Further Scope

机器学习处理各种问题。实际上，几乎所有领域都具有借助机器学习实现自动化或改进的范围。正在对此进行大量工作的此类一些问题如下。

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.

Time Series Data

这是会随着时间而改变的数据，因此时间在这里扮演着至关重要的角色，我们在本教程中广泛探讨了这一点。

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.

Non-Time Series Data

它是不随时间变化的数据，大多数 ML 问题都是非时间序列数据。为简单起见，我们将其进一步分类为 -

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 −

  1. 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.

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

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

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