Weather Forecasting Application using Time Series Analysis

Abstract

This project entails the development of a weather forecasting application utilizing data from a college's weather station. The core of the application is a machine learning model trained on time series data to forecast temperature, wind, and humidity. Integrated with Flutter for the front end and TFLite for the backend, the application provides users with an intuitive interface for accessing accurate and reliable weather forecasts. Key features include real-time updates sourced from the weather station, interactive visualizations of forecasted data and customizable theme changeable settings for weather alerts. Continuous modelrefinement ensures forecast accuracy, accessibility, and performance optimization enhance user experience. Feedback mechanisms are also provided for user engagement and assistance. This project aims to deliver a robust and user-centric weather forecasting solution leveraging machine learning and mobile app technologies.