Automatic forecasts of univariate time series are largely demanded in business and science. In this paper, we investigate the forecasting task for geo-referenced time series. We take into account the temporal and spatial dimension of time series to get accurate forecasting of future data. We describe two algorithms for forecasting which ARIMA models. The first is designed for seasonal data and ...

In this paper an ARIMA model is used for time-series forecast involving wind speed measurements. Results are compared with the performance of a back propagation type NNT. Results show that ARIMA model is better than NNT for short time-intervals to forecast (10 minutes, 1 hour, 2 hours and 4 hours). Data was acquired from a unit located in Southern Andalusia (Peñaflor, Sevilla), with a soft orog...

This paper investigates the ability of a new hybrid forecasting model based on empirical mode decomposition (EMD), cluster analysis and Autoregressive Integrated Moving Average (ARIMA) model to improve the accuracy of fishery landing forecasting. In the first step, the original fishery landing was decomposed into a finite number of Intrinsic Mode Functions (IMFs) and a residual by EMD. The seco...

5 This paper introduces Singular Spectrum Analysis (SSA) for tourism demand forecasting 6 via an application into total monthly U.S. Tourist arrivals from 1996-2012. The global 7 tourism industry is today, a key driver of foreign exchange inflows to an economy. Here, we 8 compare the forecasting results from SSA with those from ARIMA, Exponential Smoothing 9 (ETS) and Neural Networks (NN). We f...

Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investi...

Predicting daily occupancy is extremely important for the revenue management of individual hotels. However, daily occupancy can fluctuate widely and is difficult to forecast accurately based on existing forecasting methods. In this paper, Ensemble Empirical Mode Decomposition (EEMD)—a novel method—is introduced, and an individual hotel is chosen to test the effectiveness of EEMD in combination ...