Time-Series Classification

Time-series classification is a technique of machine learning that can be used for the classification of data that is collected over time. The data is represented as a sequence of values that are measured at regular intervals. Time-series classification can be applied to a wide range of problems such as predicting the weather, detecting anomalies in financial data, or recognising human activities.

Applications of Time-Series Classification

Time-series classification has many applications in the real world, some of which are as follows:

• Predictive analytics: Time-series classification can be used to predict future values of a time series. This can be useful for tasks such as forecasting demand, predicting stock prices, or forecasting weather patterns.
• Anomaly detection: Time-series classification can be used to detect anomalies in data, such as changes in sales patterns or unusual activity on a network. This can be useful for tasks such as fraud detection, equipment monitoring, or detecting cyberattacks.
• Human activity recognition: Time-series classification can be used for recognising human activities from data collected from wearable sensors. This can be used for applications such as fitness tracking, fall detection, and gesture recognition.
• Medical diagnosis: Time-series classification can be used for diagnosing medical conditions from data collected from medical sensors. This can be used for applications such as diagnosing heart disease, diabetes, and neurological disorders.
• Financial forecasting: Time-series classification can be used to forecast financial data, such as stock prices and interest rates. This can be helpful for tasks such as investment decisions, trading, and risk management.

Types of Time-Series Classification Methods

There are two main types of time series classification methods, namely distance-based methods and model-based methods.