Research news on time series analysis

Time series analysis comprises a set of statistical and computational methods for modeling, decomposing, and forecasting data indexed in time order, explicitly accounting for temporal dependence, nonstationarity, and serial correlation. Core approaches include autoregressive (AR), moving average (MA), and ARIMA-family models, state-space and Kalman filter methods, spectral and wavelet analysis, and modern machine learning–based sequence models. Typical objectives are identification of underlying stochastic processes, estimation of parameters, detection of structural breaks, and prediction of future values, while addressing issues such as seasonality, trends, heteroskedasticity, and autocorrelated residuals under rigorously defined probabilistic frameworks.

Seismic activity in California varies with the seasons

Earthquakes occur when the tectonic plates of the Earth's crust shift, jolting past each other in a release of built-up tension. However, other natural forces can also influence seismic activity: Hydrological dynamics, like ...

US weather and climate disasters could top $1 trillion by 2030

From tornadoes and hurricanes to wildfires and floods, weather and climate disasters cause billions of dollars in damage, on top of their steep human toll. Those costs could rise sharply in the years ahead, according to a ...