Time Series Forecasting with PyTorch: Predicting Stock Prices

In this project, we aim to predict stock prices using a Long Short-Term Memory (LSTM) neural network, a powerful model for time series forecasting. The LSTM model is trained and evaluated on historical stock price data to make predictions on future stock prices. The code walkthrough provides an explanation of each code segment, highlighting the key functionalities and transformations performed at different stages of the process. The project utilizes popular Python libraries such as PyTorch, NumPy, pandas, and matplotlib to handle the data, build the model, and visualize the results.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

import torch
import torch.nn as nn

data = pd.read_csv('AMZN.csv')

data

For data manipulation and visualization, this code imports pandas, numpy, and matplotlib libraries. In addition, it imports torch and torch.nn, which are Python libraries for deep learning.

Using the pandas library, the following code reads a CSV file named ‘AMZN.csv’ and stores the data in a variable named ‘data’. It is likely that the CSV file contains information about Amazon’s stock prices.

Executing this code loads the data from the CSV file into your program, making it available for further analysis.