1.Detection of Time Series Data.pptx Presentation

Time Series PPT

1. * KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 1 PROJECT PRESENTATION Class & Team No: AD&AM - 05 Review No: 05 Title: Anamoly Detection (Time Series) Date: 14-11-2024 Time:

2. * KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 2 AN A M O LY DETECTION FOR TIME SERIES DATA DONE BY Vidhya V- 22AD064 Ramesh Kumar K - 23AM052 Risvanth V - 23AM054 Smirithi - Neeraja -

3. * KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 3 Fraud and Market Manipulation Detection: Identify unusual trading patterns that could indicate insider trading, pump-and-dump schemes, or spoofing Volatility and Risk Monitoring: Detect sudden spikes or drops in stock prices, which can indicate market distress or economic news impact. Predictive Maintenance for Trading Systems: Monitor real-time data feeds to catch anomalies in data flow or system performance, ensuring high availability and accuracy in trading platforms. DOMAIN

4. KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 4 Objective: To develop an automated system for detecting anomalies in stock market data, identifying unusual trading patterns, and flagging potential risks using advanced machine learning techniques. Technology: Combines a hybrid of statistical anomaly detection algorithms and deep learning models (e.g., LSTM for time series analysis) to monitor stock price movements, volume surges, and unusual volatility in real-time. Significance: Supports financial institutions and regulators in identifying market manipulation, ensuring compliance, and mitigating potential financial risks by proactively flagging suspicious INTRODUCTION

5. * KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 5 Automatically identify unusual patterns in stock trading data to support financial market integrity. Detect and extract anomalies in trading volumes, price fluctuations, or suspicious patterns for accurate risk assessment and fraud detection. Provide a scalable solution for financial institutions and regulators to monitor and address potential market manipulation or irregular activities in real-time scenarios. OBJECTIVE

6. * KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 6 Manual monitoring of stock market anomalies is resource-intensive and often inefficient. Existing surveillance systems struggle to automatically detect unusual trading patterns and identify potential risks in real time. An effective solution is required to support financial institutions and regulators in ensuring market integrity and mitigating financial risks. PROBLEM STATEMENT

7. * KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 7 Utilize an LSTM Autoencoder to automatically detect anomalies in time-series stock data, ensuring accurate identification of unusual trading patterns. Apply the trained LSTM model to detect deviations in stock price movements, volume, or volatility, enabling early identification of potential risks. Annotate and store processed data with anomaly detection results, allowing for efficient tracking, reporting, and analysis by financial analysts and regulatory authorities. PROPOSED SYSTEM

8. 8 SYSTEM REQUIREMENTS S OFTWARE: LSTM Autoencoder Pandas Matplotlib E NVIRONMENT: Python TensorFlow or PyTorch

9. * KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 9 Model Integration: Load and configure the LSTM Autoencoder model for anomaly detection in time-series stock data. Process data in sequential batches to identify abnormal patterns, triggering alerts when anomalies are detected. Anomaly Detection Processing: Use the LSTM model to analyze time-series sequences, focusing on deviations in stock prices, trading volumes, or volatility metrics whenever anomalies are detected. Annotation and Output: Annotate data points with anomaly flags and scores, then save the processed results to a designated directory for record-keeping, further analysis, or real-time monitoring dashboards. IMPLEMENTATION DETAIL

10. * KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 10 https://drive.google.com/file/d/1S51qJepMoAG12z41al1MrIEVubZiL0oG/view?usp=sharing RESULTS( Source Code)

11. * KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 11 Real-time Anomaly Detection: The system effectively identifies unusual patterns in time-series stock data using LSTM Autoencoder models, flagging potential anomalies in trading behavior. Enhanced Accuracy with Future Training: With further training and optimization, anomaly detection accuracy can be improved, adapting to new market conditions and varied financial environments. Potential for Real-World Integration: The system can be extended for real-time deployment in financial monitoring systems, with future scope for integration into risk assessment tools and compliance auditing. CONCLUSION / FUTURE SCOPE

12. * KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 12 https://www.geeksforgeeks.org/anomaly-detection-in-time-series-data/ https://www.researchgate.net/publication/344704514_A_Review_of_Time-Series_Anomaly_Detection_Techniques_A_Step_to_Future_Perspectives REFERENCE BOOKS Charu C Aggarwal. 2017. An introduction to outlier analysis. In  Outlier analysis.  1--34. Charu C Aggarwal and Saket Sathe. 2015. Theoretical foundations and algorithms for outlier ensembles.  Acm sigkdd explorations newsletter  17, 1 (2015), 24--47 . REFERENCE

13. * KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 13 Queries.

14. 14 Thank you.