Embark on a time series analysis project using a dataset with a time component, specifically historical stock prices. The objective is to uncover patterns, trends, and insights from the temporal data, enabling a better understanding of stock price movements over time.
• Developed a time series forecasting model for Power Consumption in India using Python, Prophet, and Plotly.
• Achieved Mean Absolute Error (MAE) of 7.918 in the initial model and optimized it by adding daily and yearly seasonality, resulting in a reduced MAE of 5.600.
• Visualized actual and predicted power consumption using interactive Plotly graphs, contributing to accurate forecasting.