Temporal Distribution Characterization

Temporal Distribution Characterization: Understanding Time Series Data Temporal Distribution Characterization, or TDC, is a powerful module in the AdaRNN architecture that characterizes the distributional information in a time series. Time series data is any data that is collected over a period of time, such as stock prices, weather data, or medical data. Analyzing time series data can be difficult because the data changes over time, and the traditional statistical models may not be suitable fo

Temporal Distribution Matching

Welcome to the world of Temporal Distribution Matching (TDM)! What is TDM? Temporal Distribution Matching is a method for matching the distributions of the discovered periods to build a time series prediction model. It is used in the AdaRNN architecture, which is a type of recurrent neural network model. Why use TDM? The TDM module is designed to learn the common knowledge shared by different periods via matching their distributions. This allows the learned model to generalize well on unse

1 / 1