Sequences

Sequences are ordered sets of data points where the order of the elements conveys important information.

They are used when the sequence or order of the data points is crucial for analysis, such as in time series data, text data, or biological sequences. Sequences are commonly applied in scenarios such as natural language processing, speech recognition, financial forecasting, and DNA sequence analysis. The technique works by capturing the dependencies and patterns in the ordered data, allowing for the analysis and prediction of future elements based on past observations.

For example, in natural language processing, a sequence of words forms a sentence where the order of the words is crucial for understanding the meaning. In time series analysis, a sequence of stock prices can be used to forecast future prices based on past observations.

Tokenization is an important step in processing sequences, especially in texual data. It involves breaking down the sequence into smaller units called tokens, which can be words, phrases, or characters.

Sequences are important because they allow for the analysis and understanding of ordered data, enabling models to extract meaningful insights from the sequence of elements. It is a powerful approach in machine learning, enabling models to process and analyze data where the order of input is significant.