time‐series in general and inflation in particular, we investigate if the forecasting performance can be improved even … performance is on par with the seasonal autoregressive model SARIMA. Additionally, we conduct a sensitivity analysis with respect …

This paper contains a forecasting exercise on 30 time series, ranging on several fields, from economy to ecology. The …

This report provides estimates of structural (demographic) housing demand at a regional level. Any assessment of … structural future housing demand relies on assumptions around (1) population growth, (2) headship rates (the typical household … size) and (3) rates of obsolescence of the housing stock. Each of these key drivers of structural housing demand is subject …

Neural networks (NN) and fuzzy logic systems (FLS) are used successfully for financial forecasting, credit rating and … optimizer. The mathematical motivation for such hybrid networks is presented, using the Kolmogorov theory of metric entropy. As … options written on the S&P 500 stock index. While option pricing theory typically requires a highly complex statistical model …

. The profits generated by this cheaply replicable trading scheme cannot be expected to persist. Therefore we forecast the …

forecasting horizon we either favour a denoising step plus an ARIMA forecast or an multiscale wavelet decomposition plus an ARIMA … incorporating the wavelet transform in existing forecasting methods can improve their quality. The article aims to verify this by … characteristics. We find that wavelets do improve the forecasting quality. Depending on the data's characteristics and on the …

forecasting the term structure of interest rates. As expected, I find that using more flexible models leads to a better in … maturities and forecast horizons. Subsample analysis shows that this outperformance is also consistent over time. …

pools, and use it to investigate the relative forecasting performance of dynamic stochastic general equilibrium (DSGE … react in a timely fashion to changes in the environment, leading to real-time forecast improvements relative to other … methods of density forecast combination, such as Bayesian model averaging, optimal (static) pools, and equal weights. We show …

The increasing use of demand-side management as a tool to reliably meet electricity demand at peak time has stimulated … the growing literature on models used to study demand, consumer baseline (CBL) and demand response in the electricity … market. After characterizing the general demand models, it reviews consumer baseline based on which further study the demand …