Automatic Anomaly Detection from IoT-Time Series Dataset and Evaluation of Performance Metrics

In the current era, anomaly detection (AD) has become an important area of research. Considering that the majority of prior strategies needed domain – specific supervision to set the model parameters and that the presently used techniques only support categorical or numerical data. Consequently, there exists a necessity. For contemporary algorithms that are independent of a domain provides generalized methods for datasets with mixed attributes, as well. The proposed approach can automatically differentiate between anomalies and normal behavior when employed with data that have numeric or categorical, i.e., single data type along with data with mixed-type attributes. The main objective of the suggested work is to improve classification performance by automatically removing abnormalities. The current study employs Score Based Anomaly Detection (SBAD), a Gaussian Mixture Model-based anomaly detection technique. Buildings can record environmental data using small computers like Arduino devices, which can then be used to anticipate basic and helpful qualities. Data from IoT-based time series is gathered from the UCI repository. The data set includes the date as well as environmental parameters including light, temperature, humidity, and CO2.