Rainfall is a crucial phenomenon within a climate system, whose chaotic nature has a direct influence on water resource planning, agriculture and biological systems. Within finance, the level of rainfall over a period of time is vital for estimating the value of a financial security. In this study we evaluate seven machine learning methods for rainfall prediction in the context of weather derivatives.
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A Wavelet Network
The purpose of this study is to develop a model that describes the dynamics of the daily average temperature accurately in the context of weather derivatives pricing. More precisely, we compare two state-of-the-art machine learning algorithms, namely wavelet networks and genetic programming, with the classic linear approaches that are used widely in the pricing of temperature derivatives in the financial weather market, as well as with various machine learning benchmark models such as neural networks, radial basis functions and support vector regression. Continue reading
This is the second part of the 10 most popular question related to weather derivatives. You can read the first part here. Read the questions and answers from 6-10.
In this post I’ll try to make a brief introduction to weather derivatives in the form of Q&A. I will try to answer with simple words the ten most common questions related to weather derivatives. Read below the first part.