This paper develops a flexible approach to combine forecasts of future spot rates with forecasts from time-series models or macroeconomic variables. We find empirical evidence that accounting for both regimes in interest rate dynamics and combining forecasts from different models helps improve the out-of-sample forecasting performance for US short-term rates. Imposing restrictions from the expe...

Palm oil is the most consumed and traded vegetable oils in the EU and the world. Increasing non-food uses for vegetable oils in especially feedstock of biofuels in recent years have caused the price volatility to rise in both EU and global market. The most efficient pricing of crude palm oil (CPO) is to found on Bursa Malaysia (BMD), and it provides by far the world’s most liquid palm oil contr...

We address the problem of continuously observing and forecasting long-term semantic activities of a first-person camera wearer: what the person will do, where they will go, and what goal they are seeking. In contrast to prior work in trajectory forecasting and short-term activity forecasting, our algorithm, DARKO, reasons about the future position, future semantic state, and future high-level g...

The problem of medium to long term sales forecasting raises a number of requirements that must be suitably addressed in the design of the employed forecasting methods. These include long forecasting horizons (up to 52 periods ahead), a high number of quantities to be forecasted, which limits the possibility of human intervention, as well as frequent introduction of new articles (for which no pa...

This paper provides clear cut evidence that the slope and curvature factors of the yield curve contain more information about future changes in economic activity than the term spread alone, often used in practice as an indicator of future economic conditions. These two factors constitute independent sources of information about future economic activity, which are o¤set to each other in term spr...

Using the spectral regression method, we test for long-term stochastic memory in threeand six-month daily returns series of Eurocurrency deposits denominated in major currencies. Significant evidence of positive long-term dependence is found in several Eurocurrency returns series. Compared to benchmark linear models, the estimated fractional models result in dramatic out-of-sample forecasting i...

We describe an image processing methodology using Total Sky Imagers (TSIs) to generate short-term forecasts of Direct Normal Irradiance (DNI) at the ground level. Specifically, we are interested in forecasting 1-min averaged DNI values for time horizons varying from 3 to 15 min. This work describes several sky image processing techniques relevant to solar forecasting, including velocity field c...

I n this paper, we specify that the GARCH(1,1) model has strong forecasting volatility and its usage under the truncated standard normal distribution (TSND) is more suitable than when it is under the normal and student-t distributions. On the contrary, no comparison was tried between the forecasting performance of volatility of the daily return series using the multi-step ahead forec...

– This paper applies neural network methodology to inßation forecasting in the Euro-area and the USA. Neural network methodology outperforms linear forecasting methods for the Euro Area at forecast horizons of one, three, and six month horizons, while the linear model is preferable for US data. The nonlinear estimation shows that unemployment is a signiÞcant predictor of inßation for the Euro A...

This paper introduces the concept of Temporal Hierarchies for time series forecasting. A temporal hierarchy can be constructed for any time series by means of non-overlapping temporal aggregation. Predictions constructed at all aggregation levels are combined with the proposed framework to result in temporally reconciled, accurate and robust forecasts. The implied combination mitigates modellin...