Weather forecasting using deep learning techniques
AG Salman, B Kanigoro… - … international conference on …, 2015 - ieeexplore.ieee.org
Weather forecasting has gained attention many researchers from various research
communities due to its effect to the global human life. The emerging deep learning …
communities due to its effect to the global human life. The emerging deep learning …
Forecasting the weather of Nevada: A deep learning approach
M Hossain, B Rekabdar, SJ Louis… - 2015 international joint …, 2015 - ieeexplore.ieee.org
This paper compares two approaches for predicting air temperature from historical pressure,
humidity, and temperature data gathered from meteorological sensors in Northwestern …
humidity, and temperature data gathered from meteorological sensors in Northwestern …
Intuitionistic fuzzy time series: an approach for handling nondeterminism in time series forecasting
S Kumar, SS Gangwar - IEEE Transactions on Fuzzy Systems, 2015 - ieeexplore.ieee.org
Atanassov introduced the notion of intuitionistic fuzzy set as generalization of the fuzzy set,
which has been proved to be very useful tool in handling nondeterminacy (hesitation) in the …
which has been proved to be very useful tool in handling nondeterminacy (hesitation) in the …
Prediction of stock index futures prices based on fuzzy sets and multivariate fuzzy time series
BQ Sun, H Guo, HR Karimi, Y Ge, S Xiong - Neurocomputing, 2015 - Elsevier
This paper makes a prediction of Chinese stock index (CSI) future prices using fuzzy sets
and multivariate fuzzy time series method. We select Chinese CSI 300 index futures as the …
and multivariate fuzzy time series method. We select Chinese CSI 300 index futures as the …
A new linguistic out-sample approach of fuzzy time series for daily forecasting of Malaysian electricity load demand
The fuzzy logical relationships and the midpoints of interval have been used to determine
the numerical in-out-samples forecast in the fuzzy time series modeling. However, the …
the numerical in-out-samples forecast in the fuzzy time series modeling. However, the …
Deep neural network modeling for big data weather forecasting
The coming of the big data era brings the opportunities to greatly improve the forecasting
accuracy of weather phenomena. Specifically, weather change is quite a complex process …
accuracy of weather phenomena. Specifically, weather change is quite a complex process …
Fuzzy forecasting based on automatic clustering and axiomatic fuzzy set classification
W Wang, X Liu - Information Sciences, 2015 - Elsevier
In spite of the impressive diversity of models of fuzzy forecasting, there is still a burning need
to arrive at models that are both accurate and highly interpretable. This study proposes a …
to arrive at models that are both accurate and highly interpretable. This study proposes a …
Layered ensemble architecture for time series forecasting
Time series forecasting (TSF) has been widely used in many application areas such as
science, engineering, and finance. The phenomena generating time series are usually …
science, engineering, and finance. The phenomena generating time series are usually …
A fuzzy time series forecasting method induced by intuitionistic fuzzy sets
S Kumar, SS Gangwar - International Journal of Modeling …, 2015 - World Scientific
Intuitionistic fuzzy sets (IFSs) are well established as a tool to handle the hesitation in the
decision system. In this research paper, fuzzy sets induced by IFS are used to develop a …
decision system. In this research paper, fuzzy sets induced by IFS are used to develop a …
Application of fuzzy time series approach in electric load forecasting
In electrical power management, load forecasting accuracy is an indispensable factor which
influences the decision making and planning of power companies in the future. Previous …
influences the decision making and planning of power companies in the future. Previous …