Exploring the relationship between climate change and rice yield in Bangladesh: An analysis of time series data

2012 ◽  
Vol 112 ◽  
pp. 11-16 ◽  
Author(s):  
Md. Abdur Rashid Sarker ◽  
Khorshed Alam ◽  
Jeff Gow
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Time Series Data ◽  
Rice Yield ◽  
Series Data ◽  
The Relationship ◽  
Analysis Of Time Series

Dimensionality Reduction for the Analysis of Time Series Data from Wind Turbines

2017 ◽  
pp. 317-339 ◽  
Author(s):  
Jochen Garcke ◽  
Rodrigo Iza-Teran ◽  
Marvin Marks ◽  
Mandar Pathare ◽  
Dirk Schollbach ◽  
...  
Keyword(s):  
Time Series ◽  
Dimensionality Reduction ◽  
Wind Turbines ◽  
Time Series Data ◽  
Series Data ◽  
Analysis Of Time Series

scholarly journals PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) SEASONAL PADA DATA CURAH HUJAN EMPAT KABUPATEN DI PROVINSI JAWA TENGAH

2019 ◽  
Vol 8 (4) ◽  
pp. 418-427
Author(s):  
Eko Siswanto ◽  
Hasbi Yasin ◽  
Sudarno Sudarno
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Space Time ◽  
Series Data ◽  
Inverse Distance Weighted ◽  
Modeling And Forecasting ◽  
Distance Weighted ◽  
The Relationship ◽  
Inverse Distance

In many applications, several time series data are recorded simultaneously at a number of locations. Time series data from nearby locations often to be related by spatial and time. This data is called spatial time series data. Generalized Space Time Autoregressive (GSTAR) model is one of space time models used to modeling and forecasting spatial time series data. This study applied GTSAR model to modeling volume of rainfall four locations in Jepara Regency, Kudus Regency, Pati Regency, and Grobogan Regency. Based on the smallest RMSE mean of forecasting result, the best model chosen by this study is GSTAR (11)-I(1)12 with the inverse distance weighted. Based on GSTAR(11)-I(1)12 with the inverse distance weighted, the relationship between the location shown on rainfall Pati Regency influenced by the rainfall in other regencies. Keywords: GSTAR, RMSE, Rainfall

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