In this study, we address a demanding time series forecasting problem that deals simultaneously with the following: (1) intermittent time series, (2) multi-step ahead forecasting, (3) time series with multiple seasonal periods, and (4) performance measures for model selection across multiple...

Accurate predictions of hourly PM2.5 concentrations are crucial for preventing the harmful effects of air pollution. In this study, a new decomposition-ensemble framework incorporating the variational mode decomposition method (VMD), econometric forecasting method (autoregressive integrated...

Most macroeconomic models impose a tight link between expected future short rates and the term structure of interest rates via the expectations hypothesis (EH). While the EH has been systematically rejected in the data, existing work evaluating the EH generally assumes either full-information...

Many forecasting methods perform poorly when dealing with intermittent demand patterns and large variations in demand quantity. The presence of significant fluctuations within the time series, coupled with a substantial proportion of zero observations, complicates the extraction of trend and...

Given the unobserved nature of expectations, this paper employs latent variable analysis to examine three financial instability models and assess their out-of-sample forecasting accuracy. We compare a benchmark linear random walk model, which implies exogenous instability phenomena, with a...