Pattern Matching

What will I learn?

In this example, we walk through a straightforward approach to conduct pattern matching on time series manufacturing data using similarity search in KDB.AI, a vector database. This approach allows us to identify and retrieve historical time series that exhibit specific patterns without the need for complex modeling or domain-specific expertise.

Agenda

We begin by loading sensor data from a dataset, cleaning it and then create sensor vector embeddings by dividing the time series data into overlapping windows and normalizing the data within each window.

These embeddings are stored in a KDB.AI table, which is a vector database designed for efficient similarity searches.

To demonstrate pattern matching, we select an example pattern from the data and search for similar sequences in the KDB.AI table. We visualize these matches on a time series chart, allowing us to compare and analyze the patterns found.

Who is this suitable for?

Aimed at complete beginners to KDB.AI. No prior experience required but python knowledge will make it easier to follow along.

To access your own KDB.AI instance as shown in the video simply click blue button above or else signup here.

The Jupyter notebook shown in the video is available to download here. Note, you will need to install the requirements.txt file in the repository to make sure yo have all the relevant Python packages needed to run the code.

Got a question? Ask here on our Slack channel here!