Implementation of File-Based Data Input and Multiple Predictive Models

In this documentation, we have implemented multiple functionalities for data processing. Initially, we import data into the system through file input operations, which can be implemented using MATLAB's file reading functions like `readtable()`, `xlsread()`, or `fscanf()` depending on the file format (CSV, Excel, or text files). Subsequently, we employ the Autoregressive (AR) model for prediction, a widely-used time series forecasting method that can be implemented using MATLAB's `ar()` function or the System Identification Toolbox, where the model order is typically determined through criteria like AIC or BIC.

Furthermore, we utilize the Autoregressive Moving Average (ARMA) model for more sophisticated predictions, which combines autoregressive and moving average components to achieve higher accuracy in trend forecasting. This can be implemented using functions like `armax()` from the System Identification Toolbox, requiring proper parameter estimation through algorithms such as the maximum likelihood method.

Additionally, we incorporate the Kalman filter model for prediction, a powerful technique that adapts to dynamic system changes. In MATLAB, this can be implemented using the `kalman()` function or custom state-space modeling with functions like `ss()` and `predict()`, involving state prediction and update steps based on measurement innovations.

Finally, we developed a user-friendly graphical user interface (GUI) using MATLAB's App Designer or GUIDE framework, which includes components like file upload buttons, parameter input fields, and real-time plotting axes. This interface simplifies data processing by providing intuitive controls for model selection, parameter adjustment, and visualization of prediction results through MATLAB's plotting functions such as `plot()` and `stem()`.

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