Predicting stock prices with a deep model is an example of which type of data?

Predicting stock prices with a deep model is an example of sequential data because stock prices are often time-series data, where the sequence of past prices influences future price predictions. This means that the data points (stock prices) are interconnected in a sequence, with each value representing a specific point in time. In time-series analysis, the order of the data is crucial, as it reflects temporal dependencies; the models need to account for trends, seasonality, and patterns within the sequence of past stock prices to make accurate predictions.

Sequential data contrasts with categorical data, which involves distinct categories without any intrinsic order, discrete data, which consists of separate values typically counted (like the number of trades), and static data, which does not change over time. In the context of stock price prediction, the focus is on how the prices evolve over time rather than a mere snapshot or count, reinforcing why sequential data is the most fitting description in this scenario.