Implementation of ARIMA Model for Prediction of Airline Passengers

---

Goals

Performing ARIMA time series modeling using Python.

---

Objective

1. Import the dataset and required libraries.
2. Plot the time series data to check for patterns.
3. Check for stationarity; if the data is not stationary, perform differencing.
4. Determine the order p, q based on the ACF and PACF plots, and the order d based on the number of differences.
5. Train the ARIMA model with the determined orders p, d, q.
6. Compare the actual data with the predictions.
7. Calculate evaluation metrics such as MSE.
8. Make future predictions for a specific period.

---

Dataset Airline Passengers

This dataset contain list of airline passenger from year 1949 to 1960.

---

Tools

1. Python Programming Language
2. Google Colab

Documentation

Python Notebook Click Here!