This project focuses on analyzing hourly electricity consumption data using time series analysis techniques. The goal is to make accurate predictions and evaluate forecast performance.

1. Dataset Splitting: The dataset is divided into training and testing subsets to facilitate model training and evaluation.
2. Formatting for Prophet: Data preprocessing involves formatting the dataset into the required input format for Facebook's Prophet forecasting model.

• The training data is fitted onto the Prophet model to capture underlying patterns and seasonality.
• Predictions are made on the test data using the trained model.

• Comparison with Actuals: The forecasted values are compared with the actual consumption data to assess the accuracy of the predictions.
• Error Calculation: Mean Squared Error (MSE) and Mean Absolute Error (MAE) are calculated to quantify the discrepancy between predicted and actual values.

• A similar process is repeated, taking into account the effects of holidays on electricity consumption patterns.
• The model is trained and evaluated with holiday data to improve forecast accuracy.

• dataset/: Contains the raw dataset.
• notebooks/: Jupyter notebooks documenting the data analysis, model training, and evaluation processes.
• README.md: Overview of the project.

1. Clone the repository: git clone https://github.com/prnvpwr2612/Time-Series-Analysis-on-Hourly-Electricity-Consumption
2. Navigate to the project directory.
3. Run the provided scripts or notebooks to preprocess the data, train the model, make predictions, and evaluate forecast performance.

• Python 3.x
• Jupyter Notebook
• Pandas
• Numpy
• Prophet
• Seaborn
• Matplotlib
• Scikit-learn

• Pranav Pawar