Download the dataset below to solve this Data Science case study on forecasting multiple variables.
Time series forecasting of multiple variables, often referred to as multivariate time series forecasting, involves predicting future values of several time-dependent variables using historical data.
The given dataset consists of historical stock data for four major companies:
- Apple (AAPL)
- Microsoft (MSFT)
- Netflix (NFLX)
- and Google (GOOG)
Spanning approximately 3 months, from February 7, 2023, to May 5, 2023, the dataset includes the following features for each trading session:
- open price
- high price
- low price
- close price
- adjusted close price
- and trading volume.
These time-stamped records provide a detailed view of the stock performance over the specified period, making it a suitable candidate for time series analysis.
Your task is to forecast the future closing prices of the four stocks using their historical data. By leveraging the multivariate nature of the dataset, the goal is to model the interdependencies among the stocks and predict their future values simultaneously.
