Major Project

A full-stack application for time series data analysis and clustering with an interactive web interface.

Project Overview

This project consists of a Flask backend for time series clustering and analysis, and a React frontend for visualization and interaction. The backend uses machine learning algorithms to cluster time series data, identify peaks and valleys, and perform outlier detection.

CSV File Format Requirements

The application accepts CSV files with the following specifications:

Format

• Separator: Comma-separated values (,)
• Encoding: UTF-8 (standard)
• Header: Optional (if present, will be ignored)

Data Structure

• Each row represents a single time series (one graph/series)
• Each column represents a time point/measurement
• All values must be numeric (integers or floats)
• The data is processed row-wise, where each row is treated as an independent time series

Data Requirements

• All cells must contain valid numeric values (no text, no empty cells)
• Missing values are not supported - ensure all cells are filled
• The number of columns (time points) should be consistent across all rows
• Rows with less than 4 peaks after processing will be automatically filtered out

Example Format

266,270,273,269,271,270,268,269,269,270,...
264,265,265,264,263,264,263,264,264,262,...
328,324,325,323,324,323,323,319,324,324,...

In this example:

• Row 1, Row 2, Row 3 each represent a different time series
• Each comma-separated value represents a measurement at a specific time point
• All values are numeric

Processing Notes

• The uploaded data undergoes preprocessing including:
  • Derivative calculation
  • Normalization (mean-centered and scaled)
  • Peak detection (rows with < 4 peaks are removed)
  • Outlier detection using Local Outlier Factor
• The processed data is then used for clustering analysis

Project Structure

MajorProjectBackend/
├── backend/          # Flask backend server
│   ├── server.py     # Main backend application
│   └── requirements.txt
└── frontend/         # React frontend application
    ├── src/          # React source code
    └── package.json

Backend

Technology Stack

• Flask - Web framework
• TimeSeriesKMeans (tslearn) - Time series clustering
• scikit-learn - Machine learning utilities
• NumPy & Pandas - Data processing
• SciPy - Signal processing (peak detection)

Features

• Time series data upload and processing
• Automatic optimal cluster determination using Elbow Method
• Outlier detection using Local Outlier Factor
• Peak and valley detection
• Dynamic clustering with further cluster refinement
• Graph labeling (true/false) for clusters
• Reference graph identification

Setup

1. Navigate to the backend directory:

cd MajorProjectBackend/backend

2. Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Run the server:

python server.py

The backend server will run on http://0.0.0.0:5000 or on the URL indicated in the terminal.

API Endpoints

• POST /upload - Upload CSV file for processing
• POST /furtherCluster?cluster_no=<id> - Further cluster a specific cluster
• GET /getNoOfClusters - Get the number of clusters
• GET /getFarthestGraph?graph_id=<id> - Get the farthest graph in a cluster
• GET /getClosestGraph?graph_id=<id> - Get the closest graph in a cluster
• GET /labelTrue?graph_id=<id> - Label a cluster as true
• GET /labelFalse?graph_id=<id> - Label a cluster as false
• GET /getSeries - Get the processed time series data
• GET /getLabelGraphId - Get labels and cluster IDs

Frontend

Technology Stack

• React - UI framework
• Chart.js - Data visualization
• Axios - HTTP client
• Tailwind CSS - Styling
• PapaParse - CSV parsing

Features

• Interactive time series visualization
• Cluster visualization and exploration
• Peak and valley highlighting
• Graph comparison tools
• Cluster labeling interface

Setup

1. Navigate to the frontend directory:

cd MajorProjectBackend/frontend

2. Install dependencies:

npm install

3. Start the development server:

npm start

The frontend will run on http://localhost:3000 and automatically open in your browser.

Getting Started

1. Start the backend server:

   cd MajorProjectBackend/backend
   python server.py

2. Start the frontend (in a new terminal):

   cd MajorProjectBackend/frontend
   npm start

3. Access the application:

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:5000

License

This project is part of a Major Project for academic purposes.