Project Title: Anomaly Detection in Time Series Data using Autoencoder
Project Description: The objective of this project is to detect anomalies in time series data using Autoencoder, a type of deep neural network that can learn to encode and decode input data. Anomaly detection in time series data is important in various fields, such as finance, manufacturing, and healthcare, as it can help identify unusual patterns or events that may require further investigation.
As a student, you can start by understanding the concept of time series data and anomalies. You can then collect a dataset of time series data, such as sensor readings, stock prices, or healthcare data. The data should have both normal and abnormal instances.
You can preprocess the data, split it into training and testing sets, and use Autoencoder to build a model that can learn the normal behavior of the data. Once the model is trained, you can use it to predict the output of the testing set. Any instance that deviates significantly from the predicted output can be considered an anomaly.
You can evaluate the performance of the model using metrics such as precision, recall, and F1 score. You can also visualize the anomalies to understand their patterns and characteristics.
The final deliverable can be a report detailing the methodology, findings, and recommendations for the field of application.
- A detailed analysis of time series data and anomalies
- A deep learning model using Autoencoder to detect anomalies
- An evaluation of the model's performance using metrics such as precision, recall, and F1 score
- A visualization of the anomalies to understand their patterns and characteristics
- A comprehensive report that details the methodology, findings, and recommendations for the field of application.
Tools and Technologies:
- TensorFlow or Keras
- Matplotlib or Seaborn
Project Timeline: As a student project, the timeline can be flexible and depend on your availability. However, you can follow this timeline:
- Week 1: Understanding time series data and anomalies
- Week 2-3: Data Collection and Preprocessing
- Week 4-5: Model Development and Training
- Week 6-7: Model Evaluation and Visualization of Anomalies Week 8: Report Writing and Presentation.