Hackathon '25

Time-Series Forecasting Using REST API

Overview

This project is an end-to-end pipeline for time-series forecasting using real-world data retrieved via a REST API. The solution includes data extraction, cleaning, exploratory data analysis (EDA), model building, and deployment as an interactive web application using Streamlit.

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Problem Statement

Time-series forecasting plays a vital role in domains such as finance, weather, sales, and healthcare. This project focuses on creating a model that can generalize to any time-series dataset and predict future values. It handles the entire workflow, from API data retrieval to the deployment of a forecasting solution.

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Key Features

• Data Retrieval: Fetch time-series data using REST API.
• Data Cleaning & Preprocessing: Handle missing values, duplicates, and time zone inconsistencies.
• Exploratory Data Analysis:
  • Plot trends, seasonality, and cyclic patterns.
  • Identify stationarity using the Augmented Dickey-Fuller test.
  • Analyze patterns using heatmaps and autocorrelation.
• Model Building:
  • Train ARIMA and XGBoost models.
• Model Evaluation:
  • Evaluate models using MAE, RMSE, and MAPE metrics.
  • Generate confidence intervals and provide insights.
• Deployment: Deploy the solution using Streamlit for an interactive web interface.

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Workflow

Milestone 1: Data Retrieval

• Fetch data from a REST API using API key.
• Handle authentication, rate limits, and errors.

Milestone 2: Data Cleaning & Preprocessing

• Handle missing and duplicate data.
• Convert timestamps to a consistent format (e.g., YYYY-MM-DD HH:MM:SS).

Milestone 3: Exploratory Data Analysis

• Visualize data trends and patterns.
• Identify recurring cycles and stationarity.

Milestone 4: Model Building

• Train and fine-tune forecasting models.

Milestone 5: Forecasting

• Predict future values for specific timeframes (e.g., next day, week, or month).
• Generate visualizations with confidence intervals.

Milestone 6: Deployment

• Build and deploy an interactive web app using Streamlit.
• Provide users with an intuitive interface to upload datasets and view predictions.

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Tools and Technologies

• Programming Language: Python
• Libraries: pandas, numpy, matplotlib, seaborn, statsmodels, scikit-learn, Streamlit

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How to Run the Project

1. Clone the repository:

   git clone <repository-url>

2. Install dependencies:: pip install -r requirements.txt
3. Run the app::
   • streamlit run app.py
   • Link : https://hackathon-25-zv14fu41um.streamlit.app/
4. Open the web interface in your browser and interact with the deployed solution: