Abstract:
Industrial, medical, and scientific applications use time series anomaly detection (AD). Undiscovered anomalies in chemical or water treatment plants can harm millions. Time series analysis can show how an asset, security, or economic variable changes. It can also compare the data point’s changes to other variables over the same time period.
Anomaly detection involves finding unusual events, items, or observations that deviate from norms. Standard deviations, outliers, noise, novelties, and exceptions are data anomalies. Our Time-Series Anomaly Detector App detects sudden spikes or anomalies in manual or uploaded data, such as bank statements, company leaves, personal expenses, report values, etc. The API checks numeric values for irregular patterns or spikes.
This system requires users to register with basic information to log in. The user can manually enter numbers or upload an Excel file and select a column. The system will graph the automatically detected spikes, dips, and deviations. Save everything.
These saved data are easy to view and delete. This Dart-based project uses MSSQL and Flutter. Google maintains Dart. Flutter builds high-performance mobile apps across platforms. Microsoft’s Cognitive Services are problem-solving machine learning algorithms. Scan device folders.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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