Predicting Stock Prices with LSTM and GRU: A Step-by-Step Guide

<p>Inthis tutorial, we&rsquo;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&rsquo;ll use a dataset covering IBM&rsquo;s stock prices from 2006 to 2018.</p> <pre> import pandas as pd df = pd.read_csv(&quot;IBM_2006&ndash;01&ndash;01_to_2018&ndash;01&ndash;01.csv&quot;, index_col=&#39;Date&#39;, parse_dates=[&quot;Date&quot;])</pre> <h2>Data Visualization</h2> <p>Let&rsquo;s visualize our dataset to understand the stock price trends. We&rsquo;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>
Tags: LSTM GRU