How to run : Load the project into visual studio code. Open 3 terminals: 1st terminal: python verfinal.py 2nd terminal: python verfinal2.py 3rd terminal: python home.py Open the link you get on the 3rd terminal. Project is loaded and ready to go.
Comprehensive Forecasting System with User
Interface for Multiple Sectors
Objective Develop a complete forecasting system that not only implements and compares different time series models (ARIMA, ANN, Hybrid ARIMA-ANN) across various sectors but also includes a user-friendly front-end interface for visualizing data and forecasts.
Data Sources and Preprocessing: • Finance Sector: Monthly stock prices from the S&P 500 index. • Energy Sector: Hourly energy consumption data. • Environmental Sector: Daily atmospheric CO2 concentrations.
Preprocessing Steps: • Cleaning: Identify and impute or remove missing values. • Normalization/Standardization: Scale the data to a uniform range. • Stationarization: Apply differencing and logarithmic transformations as necessary to achieve stationarity.
Tools and Technologies:
- Backend: Python with Flask for server-side logic, handling API requests.
- Frontend: ReactJS for building a dynamic and responsive user interface, HTML/CSS for layout and styling.
- Data Science: Python with Pandas, NumPy, Matplotlib, Seaborn, Statsmodels, TensorFlow/Keras.
- Database: SQLite for storing processed data and results, enabling quick retrieval for visualization.
- Version Control: Git for code management and version control.
Model Development
ARIMA Configuration and Tuning:
• Purpose: ARIMA (Autoregressive Integrated Moving Average) is utilized to model and forecast time series data that shows levels of non-stationarity or seasonal patterns.
• Process: Identify the order of differencing (d), the number of autoregressive terms (p), and the number of lagged forecast errors in the prediction equation (q) using statistical tests like the ADF (Augmented Dickey-Fuller) test for stationarity and ACF/PACF plots for parameter estimation.
ANN Design and Training:
• Purpose: Artificial Neural Networks (ANN) are used for their ability to model complex nonlinear relationships and interactions in data, which might be missed by ARIMA.
• Process: Design neural networks with varying architectures, including different numbers of layers and neurons, to find the best fit for the dataset. Implement backpropagation to train the models using historical data.
SARIMA (Seasonal ARIMA):
• Purpose: SARIMA extends ARIMA to specifically model and forecast seasonal time series data. It accounts for both non-stationary and seasonal changes within a dataset.
• Process: Determine the seasonal order of differencing (D), the number of seasonal autoregressive terms (P), and the number of seasonal lagged forecast errors (Q) along with the non-seasonal parameters. Utilize the ADF test for overall stationarity and use seasonal ACF and PACF plots to estimate the seasonal parameters.
Exponential Smoothing (ETS):
• Purpose: Exponential Smoothing models are used to forecast time series data by applying weighted averages of past observations, with the weights decaying exponentially over time. The model is particularly effective for data with trends and seasonalities.
• Process: Select appropriate models based on data characteristics—simple, double, or triple exponential smoothing. Use error, trend, and seasonal components (ETS) to configure the model. Parameters are typically selected based on model fit criteria such as AIC or BIC.
Prophet:
• Purpose: Prophet is designed to handle time series with strong seasonal effects and historical holidays, making it ideal for daily data with multiple seasonality patterns and irregularities.
• Process: Define the model with potentially yearly, weekly, and daily seasonality components and holiday effects. Adjust the model’s flexibility by tuning the seasonality mode and adding custom seasonality if needed. The model parameters are optimized automatically using a scalable fitting procedure.
Support Vector Regression (SVR):
• Purpose: SVR applies the principles of support vector machines to regression problems, modeling nonlinear relationships in the data through the use of kernel functions.
• Process: Choose an appropriate kernel (linear, polynomial, radial basis function) and tune parameters such as C (regularization parameter) and gamma (kernel coefficient). Use cross- validation to ensure the model generalizes well to unseen data.
Long Short-Term Memory (LSTM):
• Purpose: LSTM networks are a type of recurrent neural network (RNN) suitable for sequence prediction problems. They are capable of learning order dependence in sequence prediction problems.
• Process: Design the network architecture with one or more LSTM layers, define the number of neurons in each layer, and select a backpropagation algorithm for training (typically using Adam or SGD). The model learns from sequences of historical data points, with the length of input sequences being a tunable parameter.
Hybrid Models Integration:
• Purpose: Combining ARIMA and ANN models leverages ARIMA's proficiency in capturing linear relationships and ANN's ability to model complex patterns. This integration aims to enhance overall forecast accuracy by handling residuals effectively.
• Process: Use the forecast results from the ARIMA model as input features to the ANN, which then models the residuals. This step is crucial as it allows the ANN to correct and improve the predictions based on the errors generated by the ARIMA model. Frontend Development
Interface Design:
• Purpose: To provide an intuitive and user-friendly interface that allows users to interact with the forecasting system easily, selecting datasets, initiating model comparisons, and viewing forecasts.
• Process: Design a clean and straightforward UI that includes dropdown menus for dataset selection, buttons for executing forecasts, and tabs for switching between different model views.
• Tools: Use ReactJS for developing the frontend components, ensuring responsiveness and dynamic interaction, coupled with HTML/CSS for styling.
Visualization Tools:
• Purpose: To visually present data and forecasts in a way that is easy to understand and analyze. Effective visualization helps in quickly identifying trends, patterns, and anomalies.
• Process: Implement interactive charts and graphs that update in real-time as new data is processed or forecasts are generated.
• Tools: Integrate Chart.js or D3.js for creating dynamic, interactive data visualizations that can display time series data, prediction results, and model comparisons effectively.
Interactive Dashboard:
- Select different forecasting models from a dropdown menu.
- Dynamically display graphs of the time series data, forecasts, residuals, and accuracy metrics.
- Compare results between different models side-by-side.
- Upload new data and retrain models interactively.
Testing and Validation
Model Testing:
• Purpose: To ensure the reliability and accuracy of the forecasting models.
• Process: Use historical data to validate the models' predictions. Apply techniques like cross- validation to assess the models' performance and prevent overfitting.
System Testing:
• Purpose: To confirm that both the backend and frontend of the system work as expected without errors or interruptions.
• Process: Conduct unit tests for individual components and integration tests for overall system functionality. Ensure the API correctly handles requests and responses.
Deployment
Application Deployment:
• Purpose: To make the forecasting system accessible to users.
• Process: Deploy the application to a platform that ensures that it is properly containerized and scalable.