Time Series analysis with ARIMA for historical stock data and future projections

Forecasting stock prices is difficult because of the many unknowns and diverse factors that affect the financial market. Using time series data, the study attempts to assess how well the ARIMA (Auto Regressive Integrated Moving Average) model predicts the stock prices of Maersk, a significant participant in the global shipping industry. The research methodology is based on the Box-Jenkins approach, which includes model identification, estimation, and diagnostic checking to ensure reliable predictions. To ensure reliable time series forecasting, the analysis begins with the Augmented Dickey-Fuller (ADF) test, which checks for stationarity. The Akaike Information Criterion (AIC), which measures model complexity and predictive accuracy equally, serves as a guide for selecting models. The ARIMA model’s performance is assessed by comparing predicted stock prices with actual observed values. The findings show the ARIMA model’s usefulness in financial time series analysis by showing that it can accurately predict Maersk’s stock prices. The model’s suitability for stock price forecasting is confirmed by a variety of statistical metrics that validate its predictive ability. The usefulness of ARIMA models in financial forecasting is highlighted by this study, which provides insightful information to help analysts and investors make wise decisions.