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Temperature forecasting is mainly issued in qualitative terms with the use of conventional methods, assisted by the data projected images taken by meteorological satellites to assess future trends (Paras et al., 2007). Several criteria that need to be considered when choosing a forecasting method include the accuracy, the cost and the properties of the series being forecast. Considering those criteria, it is noted that such empirical approaches that has been conducted for temperature forecasting is intrinsically costlier and only proficient of providing certain information, which is usually generalized over a larger geographical area (Paras et al., 2007). Despite of involving sophisticated mathematical models to justify the use of empirical rules, it also requires a prior knowledge of the characteristics of the input time-series to predict future events. Not only that, most temperature forecasts today have limited information about uncertainty. Yet, meteorologists often find it challenging to communicate uncertainty effectively. Regardless of the extensive use of the numerical weather method, they are still restricted by the availability of numerical weather prediction products, leading to various studies being conducted for temperature forecasting (Barry & Chorley, 1982; Paras et al., 2007)

An Application of Jordan Pi-Sigma Neural Network for the Prediction of Temperature Time Series Signal