In this tutorial, we are going to learn about how to build LSTM models for time series predictions. LSTM stands for Long-Short-Term Memory
These are models built on recurrent neural network (RNN) that are particularly effective for sequence prediction problems, such as time series forecasting.
These import statements lay the groundwork for conducting time series analysis and building a LSTM neural network model for stock price prediction. Each imported library serves a specific purpose in the data fetching, preprocessing, modeling, and visualization stages of the analysis.
Data Retrieving and Processing
First, We need to understand our data and source. If your data is piped from a stock market, you need to download the csv file. Below is the code for retrieving your data;
startdate = datetime(2012,1,1)
enddate = datetime(2019,12,17)
df = yf.download(y_symbols, start = startdate, end = enddate )
df
