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 ...

This course explores dynamical systems and the various ways to use a computer to investigate their behavior. It covers the standard computational and analytical tools used in nonlinear dynamics, ...

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 ...

1. Difference Equations -- 2. Lag Operators -- 3. Stationary ARMA Processes -- 4. Forecasting -- 5. Maximum Likelihood Estimation -- 6. Spectral Analysis -- 7 ...

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, ...

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 ...