Introduction
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.
Prerequisites
- Data Retrieval
- Data Preprocessing
- Modeling
- Visualization
- Predictions
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.
You need good machine learning models that can look at the history of a sequence of data and correctly predict what the future elements of the sequence are going to be.
We are going first to load the data from Alpha Vantage. The stock Market is for American Airlines stock market prices to make your predictions, we are going to set the ticker to "AAL".
Additionally, you also define a url_string, which will return a JSON file with all the stock market data for American Airlines within the last 20 years, and a file_to_save, which will be the file to which you save the data. You'll use the ticker variable that you defined beforehand to help name this file.
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