Forecasting Stock Prices with Advanced Recurrent Neural Networks: A Comparative Analysis of LSTM, GRU, and Hybrid Models
In this article, we will explore the use of advanced recurrent neural network models — LSTM, GRU, and a hybrid of both — to predict Amazon’s stock prices based on historical data.
Our findings suggest that these machine learning techniques hold significant potential for financial forecasting, demonstrating the capability to capture temporal dependencies in the data and model market dynamics effectively.
The LSTM, GRU, and hybrid models were trained on Amazon’s historical stock price data retrieved from Quandl and were used to predict future prices. Each model was evaluated based on its ability to closely match the actual stock prices. This study contributes to the ongoing discourse on AI’s increasing role in financial services, particularly in the stocks sector.
Future work could explore tuning these models further or investigate other machine-learning techniques to enhance prediction accuracy. Additionally, incorporating other features like market indicators or global economic factors could also provide a more comprehensive prediction model. While the results are promising, it’s important to remember that stock price prediction is inherently uncertain and should be one of many tools used in financial decision-making.
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout, GRU, Bidirectional
from keras.optimizers import SGD
import math
from sklearn.metrics import mean_squared_error
Data processing, visualization, and modeling tasks are performed using this code. It begins by importing several libraries and modules that are commonly used in these tasks. First, the ‘numpy’ library is imported with the alias ‘np’. Mathematical functions and tools for arrays and matrices are provided by Numpy. It is widely used for numerical computations in Python. The next step is to import the ‘matplotlib.pyplot’ module with the alias ‘plt’. Plots and charts are created using this module. It provides a wide range of functions to generate different types of plots and customize their appearance. The plots are also styled as ‘fivethirtyeight’.
Data visualization in this style is known for its clean and modern appearance. By using this style, the resulting plots will have a consistent aesthetic. Additionally, the ‘pandas’ library is imported as ‘pd’. The Pandas library is a powerful tool for manipulating and analyzing data. It provides data structures, such as DataFrames, and a wide range of functions for handling and processing data.
Download the source code by clicking the link in the comment section.