Seasonality often accounts for the major part of quarterly or monthly movements in detrended macro-economic time series. In addition, business cycle nonlinearity is a prominent feature of many such series too. A forecaster can nowadays consider a wide variety of time series models which describe seasonal variation and regime-switching behaviour. In this paper we examine the forecasting performa...

In this paper, we evaluate the performance of a number of forecasting models of U.S. business fixed investment spending growth over the recent 1995:1-2004:2 out-of-sample period at multiple forecast horizons. The forecasting models are based on the conventional Accelerator, Neoclassical, Average Q, and Cash-Flow models of investment spending, as well as empirical models developed more recently ...

The prices of financial futures contracts can be interpreted as forecasts of the spot rates, which will apply at the final delivery date of that contract. Financial futures contracts have been traded daily since the early 1980s and provide a substantial bank of data to test the forecasting efficiency of such contracts. Tests are carried out to examine whether the interest rates implied by the f...

Policymakers need to know whether prediction is possible and if so whether any proposed forecasting method will provide forecasts that are substantively more accurate than those from the relevant benchmark method. Inspection of global temperature data suggests that it is subject to irregular variations on all relevant time scales and that variations during the late 1900s were not unusual. In su...

In this paper, models for shortand long-term prediction of wind farm power are discussed. The models are built using weather forecasting data generated at different time scales and horizons. The maximum forecast length of the short-term prediction model is 12 h, and the maximum forecast length of the long-term prediction model is 84 h. The wind farm power prediction models are built with five d...

This work describes a new hybrid method that combines information from processed satellite images with Artificial Neural Networks (ANNs) for predicting global horizontal irradiance (GHI) at temporal horizons of 30, 60, 90, and 120 min. The forecast model is applied to GHI data gathered from two distinct locations (Davis and Merced) that represent well the geographical distribution of solar irra...

This paper uses small set of variables-real GDP, the inflation rate, and the short-term interest rate -and a rich set of models -athoeretical and theoretical, linear and nonlinear, as well as classical and Bayesian models -to consider whether we could have predicted the recent downturn of the US real GDP. Comparing the performance by root mean squared errors of the models to the benchmark rando...

Electricity load prediction is an essential tool for power system planning, operation and management. The critical information it provides can be used by energy providers to maximise efficiency minimise costs. Long Short-Term Memory (LSTM) Support Vector Machine (SVM) are two suitable methods that have been successfully analysing time series problems. In this paper, the algorithms explored furt...