Time series forecasting with deep learning – LSTM

This section will give you insights into advanced time series forecasting techniques using deep learning models. Whether you’re working with traditional time series data or more complex, high-dimensional data, these deep learning models can help you make more accurate predictions. In particular, we will cover the Long Short-Term Memory (LSTM) method using keras.

We will be using keras with a tensorflow backend, so you need to install both libraries:

1. As always, let’s load the necessary libraries and preprocess some time series data:

   import numpy as np
   import pandas as pd
   import matplotlib.pyplot as plt
   from keras.models import Sequential
   from keras.layers import LSTM, Dense
   from sklearn.preprocessing import MinMaxScaler
   # Load the time series data (replace with your data)
   time_series_data = pd.read_excel('time_series_data.xlsx')
   # Normalize the data to be in the range [0, 1]
   scaler = MinMaxScaler...