Predicting Stock Prices with LSTM and GRU: A Step-by-Step Guide
<p>Inthis tutorial, we’ll dive into the exciting world of stock price prediction using Long Short-Term Memory (LSTM) neural networks. LSTM is a powerful deep-learning technique for time series forecasting, making it ideal for predicting stock prices.</p>
<p><img alt="" src="https://miro.medium.com/v2/1*NKhwsOYNUT5xU7Pyf6Znhg.png" style="width:700px" /></p>
<p>LSTM is just multiple copies of itself</p>
<h2>Getting the Data</h2>
<p>To get started, we need historical stock price data. We can obtain this data from sources like Yahoo Finance or Alpha Vantage. For this tutorial, we’ll use a dataset covering IBM’s stock prices from 2006 to 2018.</p>
<pre>
import pandas as pd
df = pd.read_csv("IBM_2006–01–01_to_2018–01–01.csv", index_col='Date', parse_dates=["Date"])</pre>
<h2>Data Visualization</h2>
<p>Let’s visualize our dataset to understand the stock price trends. We’ll plot the training set (before 2017) and the test set (2017 and beyond) separately.</p>
<p><a href="https://medium.com/@shubhangorei/predicting-stock-prices-with-lstm-and-gru-a-step-by-step-guide-381ec1554edf">Click Here</a></p>