Time Series Data Labeling: A Complete Know-How for Efficient AI Implementation
In order to build high-quality AI models, data labeling is essential. Learn how to label time series data from sensors and IoT devices quickly and easily with this blog post.
Machines and production processes are becoming increasingly digital, allowing for an array of opportunities, such as early fault detection and usage-based pricing. Sensor data analytics can identify current machine operations in real-time based on such applications.
What is Time Series Data?
Time series data, which is also said to as time-stamped data, is a sequence of data points tabulated in an order of time. It is collected at different points in time. If the data values are recorded in a meaningful sequence, such as daily stock market prices, then you have time-series data. These data points typically consist of consecutive measurements created from a similar source over a time period and are used to keep track of changes happening over time.
In addition to grabbing and marking time series data at regular intervals, it can also be captured whenever it occurs — irrespective of the time interval, as we see in logs. Logs, as a matter of fact, are a registry of processes, events, methods, functions, and operations going on among software applications, operating systems, and mechanical procedures. Executable files then produce a set of logs where all actions and operations are marked.
Log data is an essential context-dependent source to predict, prioritize and address issues. As an example, in networking, event logs help provide data about network traffic, usage, and other states of occurrences.
Here are some additional examples of time-series data:
The meteorological data such as humidity, temperature, rainfall, and other environmental variables are said to be the true examples of time series data. Analysis of on-site recorded rainfall height and volume represents time series data.
The price of wheat each year for the past 50 years, adjusted for inflation. These time trends might be useful for long-range planning to the extent that the variation in future events follows the patterns of the past.
Retail sales recorded monthly for the past 20 years. This data set has a structure showing generally increasing activity over time, as well as a distinct seasonal pattern, with peaks around the December holiday season.
Processing Time Series Data for AI Implementation
The information about the states must explicitly be available for sensor data from the past in order for a machine learning algorithm to recognize meaningful operation states. It is often necessary to produce data about these states before training the AI. The process of labeling data is known as data labeling. A quality label greatly influences the efficiency of AI-powered machine learning models. The first step to optimizing AI should be to eliminate labeling errors.
Usually, tagging sensor data requires knowledge about the input data as well as the domain. For example, industrial process experts tend to be the only ones capable of interpreting patterns in machine data. Labeling of data is, therefore, an important part of industrial data science projects. There are few tools for labeling massive high-dimensional sensor time series because they are often focused mainly on images and text.
Time Series Data Labeling Techniques
A majority of time series datasets tend to have a lot of points. Therefore the tool has to scale well to handle the situation when you have more than 100K points. Select and label patterns in time series visualizations and other interactive diagrams with a single click. We use pattern search to label all time-bound occurrences of selected patterns repeatedly on new data and then audit and correct time series label together with domain experts.
When we have an enormous number of datapoint we split them into chunks and render them first, which helps us achieve outstanding performance when the number of data points is vast. Finally, retrieve labels from Python, Matlab, and R using a single line of code or export them to Excel/CSV.
Outsource Time Series Labeling to Cogito
Labeling time series data, in general, takes too much industry expertise. Cogito, being a leading player in the data annotation and labeling domain, has developed proficiency in labeling data of events, processes, methods, and procedures occurring over a period of time. Experts here work with a set of modern time series labeling tools while also taking on the labeling tasks with manual approaches for maximum accuracy and precision of the labeled data.
Outsourcing time series labeling to Cogito puts our prospective clients in a profitable transaction as we promise the quality of data labeling at a price reasonable to your budget. If you are looking forward to partnering with a trustworthy time series data labeling expert for marking the key points of your time-denoted industrial procedures, methods, and graphical representations.
Hopefully, the above note has provided you with enough insight into the time series data and labeling processes. Having understood what time series data is and why the quality of time series datasets matters when it comes to preparing the training data ready for automated process monitoring applications and other machine learning models.