TY - GEN
T1 - Visualizing Weather Financial Impact on Industries and Weather Derivatives
AU - Bondarenko, Inga
AU - Phapale, Priyanka Bhushan
AU - Robila, Stefan
PY - 2019/5/1
Y1 - 2019/5/1
N2 - With the accelerating generation of enormous datasets, data processing tools are also increasing in computational efficiency as well as accuracy. Along with data mining and clustering techniques, tools that enable data visualization are also becoming mainstream mechanisms for extracting the maximum available useful information out of data. This is especially true for financial systems. Analyzing all available information and predicting short and long-term market trends with their fluctuations has been the cornerstone of trading strategies and giving investment advice for years. One of the interesting financial instruments, which can potentially utilize big data analysis and algorithms in several different ways are weather derivatives. Unlike other financial instruments, their research combines both market and weather analysis. This is quite complex and fits perfectly as an area we can explore big data processing and visualization capabilities. In this paper, we analyze the relationship between weather derivatives and weather and investigate how predicting the weather leads to predicting index values and therefore prices for weather derivatives. In pursuing this, we develop a web-based tool that employs weather prediction tools and use it to visualize and predict weather future prices. This research provides an example of how converging data streams can be efficiently analyzed.
AB - With the accelerating generation of enormous datasets, data processing tools are also increasing in computational efficiency as well as accuracy. Along with data mining and clustering techniques, tools that enable data visualization are also becoming mainstream mechanisms for extracting the maximum available useful information out of data. This is especially true for financial systems. Analyzing all available information and predicting short and long-term market trends with their fluctuations has been the cornerstone of trading strategies and giving investment advice for years. One of the interesting financial instruments, which can potentially utilize big data analysis and algorithms in several different ways are weather derivatives. Unlike other financial instruments, their research combines both market and weather analysis. This is quite complex and fits perfectly as an area we can explore big data processing and visualization capabilities. In this paper, we analyze the relationship between weather derivatives and weather and investigate how predicting the weather leads to predicting index values and therefore prices for weather derivatives. In pursuing this, we develop a web-based tool that employs weather prediction tools and use it to visualize and predict weather future prices. This research provides an example of how converging data streams can be efficiently analyzed.
KW - big data
KW - futures prices prediction
KW - weather derivatives
KW - weather prediction algorithm
UR - http://www.scopus.com/inward/record.url?scp=85072811497&partnerID=8YFLogxK
U2 - 10.1109/LISAT.2019.8817335
DO - 10.1109/LISAT.2019.8817335
M3 - Conference contribution
T3 - 2019 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2019
BT - 2019 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2019
Y2 - 3 May 2019
ER -