Precise reporting from imprecise data sources
Telemetry, a 19th Century concept with 21st Century application continues to expand its horizons with technology enablement. Expectations on reliable data and decision making based on data have increased but the environments in which telemetric devices operate remain as hostile as ever.
In applications where reports are compiled from time series data emanating from telemetric devices, erroneous or missing data points undermine the accuracy and usage of these reports.
Introducing Ovidius Smart Data, Sidero has harnessed machine learning techniques to auto-correct and compensate for erroneous or missing data points, providing the means for reliable and accurate reports.
Data is segmented into component parts and uploaded securely into AWS S3 buckets and mapped with AWS SageMaker. Using ARIMA and linear regression model, time series data flow/ velocity is analysed to identify anomalies and to predict correct values based on previously observed values.
Predicted values are checked with error rate to evaluate the model performance for accuracy of data. The predicted data sets are converted into CSV files and stored in S3 buckets to upload to AWS QuickSight for data visualisation and presentation.
