@techreport{oai:ipsj.ixsq.nii.ac.jp:00218721,
 author = {Mujawamariya, Marie Grace and Toshiyuki, Amagasa and Naoya, Fukuda and Mujawamariya, Marie Grace and Toshiyuki, Amagasa and Naoya, Fukuda},
 issue = {1},
 month = {Jun},
 note = {Multivariate time series prediction approaches have been significant in wide range of real-world application. This paper presents Neural Networks forecasting techniques which take as input multivariate time series data captured by sensors inside a greenhouse, and satellite weather data and predict environmental parameters such as temperature inside the greenhouse over 6 hours and 12 hours in the future. We report results of the prediction model and evaluate our models predictions accuracy based on MAE metric and standard deviation for the stability. Finally, we highlight and discuss about microclimate parameters that have significant effect for predicting precise temperature inside the greenhouse., Multivariate time series prediction approaches have been significant in wide range of real-world application. This paper presents Neural Networks forecasting techniques which take as input multivariate time series data captured by sensors inside a greenhouse, and satellite weather data and predict environmental parameters such as temperature inside the greenhouse over 6 hours and 12 hours in the future. We report results of the prediction model and evaluate our models predictions accuracy based on MAE metric and standard deviation for the stability. Finally, we highlight and discuss about microclimate parameters that have significant effect for predicting precise temperature inside the greenhouse.},
 title = {Greenhouse Microclimate Prediction based on Neural Networks},
 year = {2022}
}