In this paper we propose to apply wavelet theory in forecasting economic time series. The method consists in decomposing the series into its long-term trend and its seasonal component according to the shape of the scalogram of the discrete wavelet transform of the series. Each component is then extended to provide a forecast of the total series. The method is applied to the data set of monthly ...

A novel application of a technique developed from chaos theory is used in describing seasonal to interannual climate predictability over the Northern Hemisphere (NH). The technique is based on an empirical forecast scheme-local approximation in a reconstructed phase space-for time-series data. Data are monthly 500 hPa heights on a latitude-longitude grid covering the NH from 20°N to the equator...

The Meteo-France seasonal forecasting system 7 provides a 7-month forecast range with 25 ensembles. precipitation re-forecast (from May to November 1993–2015) was evaluated by the Brier score in terms of accuracy and reliability based on tercile probabilities. Multiple analyses were performed assess robustness score. These results show that spatial distribution depends significantly thresholds,...

During the past few years, the market for organic food has been experiencing rapid growth. However, the market demand for organic food typically fluctuates due to its seasonal nature and customized characteristics, and it remains fairly difficult to precisely forecast market demand prior to the selling season. Forecast bias usually creates inefficiency in an organic food producer’s production p...

Recently, the Particle Swarm Optimization (PSO) technique has gained much attention in the field of time series forecasting. Although PSO trained Artificial Neural Networks (ANNs) performed reasonably well in stationary time series forecasting, their effectiveness in tracking the structure of non-stationary data (especially those which contain trends or seasonal patterns) is yet to be justified...

Recent developments in dynamical seasonal forecasting of potential relevance to agricultural management are discussed. These developments emphasize the importance of using a fully probabilistic approach at all stages of the forecasting process, from the dynamical ocean–atmosphere models used to predict climate variability at seasonal and interannual time scales, through the models used to downs...