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Volume 11, Issue 11 (November 2025)

Synethetic Time Series Generator For Anamoly Detection

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7.883
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Volume 12 Issue 07

July 2026

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Author(s)

Ms.Archana Parv Jain

Abstract

Industries Increasingly Depend On Continuous Monitoring Systems Where Fast And Accurate Anomaly Detection Is Crucial For Preventing Failures And Ensuring Operational Reliability. This Project Proposes A Lightweight Python-based Synthetic Time Series Generator Capable Of Producing Realistic Data With Trends, Seasonality, Noise, And Multiple Anomaly Types. The System Supports Controlled Anomaly Injection And Automatic Labeling, Making It Suitable For Training And Evaluating Anomaly Detection Models. Experimental Results Show Strong Performance, Achieving ROC-AUC Values Between 0.85–0.95 And High Precision, Recall, And F1-scores.

Keywords

Synthetic Time Series Anomaly Detection Anomaly Injection Time Series Generator Machine Learning Deep Learning Trend Modeling Seasonality Noise Simulation Python.

Paper ID

IJSARTV11I11104304

Publication Date

November 18, 2025

Research Area

Artificial Intelligence And Data Science

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