Atmospheric Temperature Prediction using Support Vector Machines

Abstract

Abstract-Weather prediction is a challenging task for researchers and has drawn a lot of research interest in the recent years. Literature studies have shown that machine learning techniques achieved better performance than traditional statistical methods. This paper presents an application of Support Vector Machines (SVMs) for weather prediction. Time series data of daily maximum temperature at a location is analyzed to predict the maximum temperature of the next day at that location based on the daily maximum temperatures for a span of previous n days referred to as order of the input. Performance of the system is observed over various spans of 2 to 10 days by using optimal values of the kernel function. Non linear regression method is found to be suitable to train the SVM for this application. The results are compared with Multi Layer Perceptron (MLP) trained with back-propagation algorithm and the performance of SVM is found to be consistently better.

An Enhanced Support Vector Regression Model for Weather Forecasting

“…Regression is the problem of estimating a function based on a given data set. [4] Consider a data set where xi is the input vector, d i is the desired result and N corresponds to the size of the data set. The general form of Support Vector Regression estimating function is f = ∅ + where w and b are the co-efficient that have to be estimated from data, where f(x) is the non linear function in feature space.…”

Jordan Pi-Sigma Neural Network for Temperature Prediction

“…NN techniques have been frequently used for prediction (Radhika & Shashi, 2009;Smith et al, 2009), pattern recognition (Isa & Mamat, 2011), signal processing (Li & Adali, 2008;Nielsen et al, 2009) and classification (Belacel & Boulassel, 2004;Yuan-Pin et al, 2007). NN present a number of advantages over conventional empirical methods.…”

“…Indeed, MLP are prone to overfit the data (Radhika & Shashi, 2009) and adopts computationally intensive training algorithms. On the other hand, MLP also suffer long training time and often reach local minima (Ghazali & al-Jumeily, 2009).…”

An Efficient Weather Forecasting System using Artificial Neural Network

“…Y.Radhika and M.Shashi [3] presents an application of Support Vector Machines (SVMs) for weather prediction. Time series data of daily maximum temperature at location is studied to predict the maximum temperature of the next day at that location based on the daily maximum temperatures for a span of previous n days referred to as order of the input.…”