HIGH-DIMENSIONAL PROBABILISTIC TIME SERIES PREDICTION VIA DEEP LEARNING MODELS

Abstract

A time series refers to a collection of data points that are arranged in chronological order and collected over a sequence of time. Time series prediction involves predicting future values based on historical records. The applications of time series prediction span across diverse domains, including stock market trends, traffic flow patterns, monitoring of COVID-19 cases, and tracking global temperature changes. The advent of the IoT era results in the generation of high-dimensional time series data, which are acquired simultaneously from various sources through multiple sensors or channels. Traditional statistical models have limitations in handling the complexity, non-linearity, adaptability, and high dimensionality of real-world time series data.