SARIMA and Prophet for Apple Stock Price Forecasting
Predicting stock prices plays a crucial role in the financial world, enabling investors to make informed decisions.
The complex dynamics of stock market data have been captured by various forecasting models in recent years. For time series forecasting, Seasonal ARIMA and Facebook’s Prophet are two prominent models. This study analyzes these models comprehensively, focusing specifically on predicting Apple Inc. stock prices. It is AAPL. In this report, we evaluate the performance of both models, compare their strengths and weaknesses, and provide actionable insights for investment decisions.
Preparing the data, splitting it into training and test sets, and applying the appropriate models is the first step in our analysis. We implement the Seasonal ARIMA model with optimized parameters, taking into account the seasonality and trend of the AAPL stock price. Based on residual analysis, diagnostic plots, and statistical tests, we evaluate the model’s accuracy and diagnose its performance. Similarly, we employ Facebook’s Prophet, a flexible and user-friendly forecasting tool, to capture the underlying trends and patterns in the data. We discover key insights from our analysis. The seasonal ARIMA model provides robust predictions of AAPL stock prices, capturing seasonality and abrupt changes. Prophet, on the other hand, demonstrates simplicity and efficiency in implementation, making it accessible even to users with limited statistical knowledge. We observe, however, that Prophet is prone to overfitting, resulting in less accurate predictions, especially when capturing seasonality.
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