Time Series Forecasting Model

Project Overview

This forecasting model was built to help businesses predict future trends based on historical time series data. It incorporates multiple forecasting algorithms and can handle seasonal patterns, making it ideal for sales forecasting, inventory management, and resource planning.

Key Features

• •Multiple forecasting algorithms (ARIMA, Prophet, LSTM)
• •Seasonal decomposition for pattern recognition
• •Anomaly detection in time series data
• •Confidence intervals for forecast reliability
• •Interactive visualization of predictions