WWW ‘21: The Web Conference 2021 REST: Relational Event-Driven Stock Trend Forecasting REST, an event-driven stock trend forecasting framework, that overcomes two limitations of existing event-driven models. Models the stock context, and learns the effect of event information on the stocks under different contexts. Constructs a stock graph and designs a new propagation layer to propagate the effect of event information from related stocks.
The value of stock trend forecasting is not unanimous, e....

To classify something, find things that are similar and label it with the same class as the most similar thing.
The feature space is \(N-d\), where \(N\) is the number of features. Each instance is mapped to a point. The descriptive features become the axes.
The Similarity Metric Mathematically, it must conform to these 4 criteria:
Non-negative: \(f(a, b) \ge 0\) Identity: \( f(a, b) = 0 \iff a = b \) Symmetry: \( f(a, b) = f(b, a) \) Triangular inequality: \( f(a, b) \le f(a, c) + f(c, b) \) Why are non-negativity and triangular inequality important?...

To classify something, find things that are similar and label it with the same class as the most similar thing.
The feature space is \(N-d\), where \(N\) is the number of features. Each instance is mapped to a point. The descriptive features become the axes.
The Similarity Metric Mathematically, it must conform to these 4 criteria:
Non-negativity: \(f(a, b) \ge 0\) Identity of Indiscernables: \( f(a, b) = 0 \iff a = b \) Symmetry: \( f(a, b) = f(b, a) \) Subaddivity (Triangular inequality): \( f(a, b) \le f(a, c) + f(c, b) \) Why are non-negativity and triangular inequality important?...