The task being addressed in this paper consists of trying to\ud forecast the future value of a time series variable on a certain geographical\ud location, based on historical data of this variable collected on both\ud this and other locations. In general, this time series forecasting task can\ud be performed by using machine learning models, which transform the\ud original problem into a regression task. The target variable is the future\ud value of the series, while the predictors are previous past values of the\ud series up to a certain p-length time window. In this paper, we convey\ud information on both the spatial and temporal historical data to the predictive\ud models, with the goal of improving their forecasting ability. We\ud build technical indicators, which are summaries of certain properties of\ud the spatio-temporal data, grouped in the spatio-temporal clusters and\ud use them to enhance the forecasting ability of regression models. A case\ud study with air temperature data is presented
The analysis of spatial autocorrelation has defined a new\ud paradigm in ecology. Attention to spatial pattern leads to insights that\ud would otherwise overlooked, while ignoring space may lead to false conclusions\ud about ecological relationships. In this paper, we propose an\ud intelligent forecasting technique, which explicitly accounts for the property\ud of spatial autocorrelation when learning linear autoregressive models\ud (ARIMA) of spatial correlated ecologic time series. The forecasting\ud algorithm makes use of an autoregressive statistical technique, which\ud achieves accurate forecasts of future data by taking into account temporal\ud and spatial dimension of ecologic data. It uses a novel spatial-aware\ud inference procedure, which permits to learn the autoregressive model by\ud processing a time series in a neighborhood (spatial lags). Parameters of\ud forecasting models are jointly learned on spatial lags of time series. Experiments\ud with ecologic data investigate the accuracy of the proposed\ud spatial-aware forecasting model with respect to the traditional one.The analysis of spatial autocorrelation has defined a new paradigm in ecology. Attention to spatial pattern leads to insights that would otherwise overlooked, while ignoring space may lead to false conclusions about ecological relationships. In this paper, we propose an intelligent forecasting technique, which explicitly accounts for the property of spatial autocorrelation when learning linear autoregressive models (ARIMA) of spatial correlated ecologic time series. The forecasting algorithm makes use of an autoregressive statistical technique, which achieves accurate forecasts of future data by taking into account temporal and spatial dimension of ecologic data. It uses a novel spatial-aware inference procedure, which permits to learn the autoregressive model by processing a time series in a neighborhood (spatial lags). Parameters of forecasting models are jointly learned on spatial lags of time series. Experiments with ecologic data investigate the accuracy of the proposed spatial-aware forecasting model with respect to the traditional one. © Springer International Publishing Switzerland 2013
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