In-Depth Analysis

S&P 500 Stock Price Prediction Using Machine Learning and Deep Learning

Time Series Forecasting methods including Simple Moving Average, Linear Regression, k-Nearest Neighbors, Auto ARIMA, Prophet, Long Short Term Memory

Written by Shiyan Boxer

Photo by Shiyan Boxer

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• GitHub

Tools

• Python — a programming language
• Pandas — data manipulation and analysis library
• NumPy — scientific computing library
• Matplotlib — plotting library
• Jupyter Notebook — create documents that contain live code
• Yahoo Finance API — retrieve stock price data

Overview

This project applies machine learning (ML) and deep learning (DL) techniques, specifically, the application of time series forecasting to predict day to day closing prices of the S&P 500. The following methods, from simple algorithms to advanced techniques were explored.

• Moving Average
• Linear Regression
• K-Nearest Neighbors (kNN)