Nonlinear cointegration and time series analysis represent a dynamic area of research that extends the classical framework of cointegration by allowing the long-run equilibrium relationships among ...

Various time-series decomposition techniques, including wavelet transform, singular spectrum analysis, empirical mode decomposition and independent component analysis, have been developed for ...

Vector time series data are widely met in practice. In this paper we propose a multivariate functional-coefficient regression model with heteroscedasticity for modelling such data. A local linear ...

Many frequently observed real-world phenomena are nonlinear in nature. This means that their output does not change in a manner that is proportional to their input. These models have a degree of ...

Time series analysis involves identifying attributes of your time series data, such as trend and seasonality, by measuring statistical properties. From stock market analysis to economic forecasting, ...

Seeking to reduce the computing power needed for the widely used dynamic mode decomposition algorithm, a team of researchers in China led by Guo-Ping Guo developed a quantum-classical hybrid algorithm ...

Artificial intelligence (AI) technologies are currently revolutionizing industries and enabling automation on a scale we've never seen before. Of course, none of this is possible without data. These ...

SAN FRANCISCO--(BUSINESS WIRE)--Planet Labs PBC (NYSE: PL), a leading provider of daily data and insights about Earth, today released Analysis-Ready PlanetScope (ARPS). ARPS harnesses a cutting-edge ...

Time series forecasts are used to predict a future value or a classification at a particular point in time. Here’s a brief overview of their common uses and how they are developed. Industries from ...