End-to-End Stock Price Forecasting: Apple Stock Analysis (2018-2019)

This project demonstrates an end-to-end time series forecasting analysis of Apple stock prices, leveraging ARIMA and Facebook Prophet models. The analysis includes exploratory data visualization, model building, and forecasting for unseen test data, with model evaluation based on Root Mean Squared Error (RMSE).

Problem Statement

In today's data-driven world, predicting stock market behavior is critical for informed decision-making. This project forecasts Apple stock prices using historical data from NASDAQ for 2018–2019. The goal is to build and evaluate models to accurately predict stock prices for future timestamps.

Objectives

1. Perform Exploratory Data Analysis (EDA) to understand the stock price trends and patterns.
2. Build and compare ARIMA models (with and without exogenous variables) for forecasting.
3. Implement Facebook Prophet, a versatile time series forecasting library.
4. Evaluate model performance using RMSE.

Methodology

1. Data Preparation

• Load and preprocess historical stock price data.
• Split data into training and testing sets.

2. Exploratory Data Analysis

• Analyze trends, seasonality, and patterns in stock prices.
• Visualize historical data using line charts and seasonal decomposition.

3. Forecasting Models

ARIMA (AutoRegressive Integrated Moving Average)

• Built ARIMA models with optimized parameters.
• Extended analysis to ARIMA with exogenous variables for enhanced predictions.

Facebook Prophet

• Used Prophet's in-built capabilities for seasonal and trend decomposition.
• Generated forecasts with confidence intervals.

4. Model Evaluation

• RMSE was used as the evaluation metric to compare model performance on the test set.

Key Results

• ARIMA Model: Delivered reliable predictions with low RMSE.
• Facebook Prophet: Demonstrated robust forecasting with intuitive interpretability of trends and seasonality.

Tools and Libraries

• Python: pandas, numpy, matplotlib, seaborn
• Forecasting Models: statsmodels (ARIMA), fbprophet
• Evaluation Metric: Root Mean Squared Error (RMSE)