Stock Market + Time Series LSTM = Failure

I will show you how to predict the stock market with LSTM and time series in this article. The basic machine learning models ruin the result. This article is based on my experience, as I have been writing on this topic for quite some time now. Let’s start by importing libraries: <pre> import numpy as np import pandas as pd import datetime import matplotlib.pyplot as plt from matplotlib.pylab import rcParams import seaborn as sns from numpy.random import seed seed(0)</pre> In this code, we are importing several libraries which will help us with data visualisation and data analysis. We start with import numpy as np; this library is for mathematical operations in python. After that, we import pandas as pd, this is a powerful library that is used for data manipulation and data analysis. This will provide us with data structures and functions that can efficiently handle and analyze large datasets. After that, we import the datetime module, which will be used for manipulating dates and times in Python. We also import pytplot module from matplotlib library, this is a widely used library used for creating static, animated and interactive visualisations in Python. We also import rcParams from matplotlib, this modules allows us with customization of plot paramters, such as the size of the figure and the resolution of the output. And at the end, we import seaborn library as sns, this library is built on top of matplotlib and provides us with a high-level interface which we can use to create visually appealing and informative statistical graphics. At the end, we import numpy.random module’s seed function, and we are going to assign it to the name seed. The seed function is used to seed the random number generator, which ensures that the same random numbers are generated each time we run the code. In our cases and as per our need, we set it to zero. <a href="https://medium.com/@redeaddiscolll/stock-market-time-series-lstm-failure-ee1bf47933f9">Read More</a>