Imputing missing values using Inverse Distance Weighted Interpolation for time series data

2014 ◽  
Author(s):  
A. T. Sree Dhevi
Keyword(s):  
Time Series ◽  
Missing Values ◽  
Time Series Data ◽  
Series Data ◽  
Weighted Interpolation ◽  
Inverse Distance Weighted ◽  
Distance Weighted ◽  
Inverse Distance Weighted Interpolation ◽  
Inverse Distance

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

scholarly journals Interpolation of Daily Temperature in Finland

2003 ◽  
Vol 34 (5) ◽  
pp. 413-426 ◽  
Author(s):  
Antti Taskinen ◽  
Hannu Sirviö ◽  
Bertel Vehviläinen
Keyword(s):  
Daily Temperature ◽  
Alternative Methods ◽  
Weighted Interpolation ◽  
Inverse Distance Weighted ◽  
Distance Weighted ◽  
Inverse Distance Weighted Interpolation ◽  
Temperature Interpolation ◽  
Forecasting System ◽  
The Difference ◽  
Inverse Distance

The present approach for daily temperature interpolation of the Watershed Simulation and Forecasting System of the Finnish Environment Institute is based on the inverse distance weighted interpolation. In order to improve the calculation, three alternative methods were tested: 1) modified inverse distance weighted model, 2) regression with dummy variables for taking into account time and 3) regression equation calibrated for each day. The regression model calibrated for each day proved to be the most promising model. By average, the difference between the accuracy of it and the inverse distance weighted methods wasn't big but some indication was found that in single cases it can make a difference. The estimated parameters were found to have realistic physical meanings.

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