Seasonal time-series imputation of gap missing algorithm (STIGMA)

@article{Rangel2023,
abstract = {This work presents a new approach for the imputation of missing data in weather time-series from a seasonal pattern; the seasonal time-series imputation of gap missing algorithm (STIGMA). The algorithm takes advantage from a seasonal pattern for the imputation of unknown data by averaging available data. We test the algorithm using data measured every $10$ minutes over a period of $365$ days during the year 2010; the variables include global irradiance, diffuse irradiance, ultraviolet irradiance, and temperature, arranged in a matrix of dimensions $52,560$ rows for data points over time and $4$ columns for weather variables. The particularity of this work is that the algorithm is well-suited for the imputation of values when the missing data are presented continuously and in seasonal patterns. The algorithm employs a date-time index to collect available data for the imputation of missing data, repeating the process until all missing values are calculated. The tests are performed by removing $5\%$, $10\%$, $15\%$, $20\%$, $25\%$, and $30\%$ of the available data, and the results are compared to autoregressive models. The proposed algorithm has been successfully tested with a maximum of $2,736$ contiguous missing values that account for $19$ consecutive days of a single month; this dataset is a portion of all the missing values when the time-series lacks $30\%$ of all data. The metrics to measure the performance of the algorithms are root-mean-square error (RMSE) and the coefficient of determination ($R^\{2\}$). The results indicate that the proposed algorithm outperforms autoregressive models while preserving the seasonal behavior of the time-series. The STIGMA is also tested with non-weather time-series of beer sales and number of air passengers per month, which also have a cyclical pattern, and the results show the precise imputation of data.},
author = {Rangel-Heras, Eduardo, Zuniga, Pavel, Alanis, Alma Y., Hernandez-Vargas, Esteban A., Sanchez, Oscar D.},
journal = {Kybernetika},
keywords = {contiguous missing values; seasonal patterns; time-series},
language = {eng},
number = {6},
pages = {861-879},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Seasonal time-series imputation of gap missing algorithm (STIGMA)},
url = {http://eudml.org/doc/299203},
volume = {59},
year = {2023},
}

TY - JOUR
AU - Rangel-Heras, Eduardo
AU - Zuniga, Pavel
AU - Alanis, Alma Y.
AU - Hernandez-Vargas, Esteban A.
AU - Sanchez, Oscar D.
TI - Seasonal time-series imputation of gap missing algorithm (STIGMA)
JO - Kybernetika
PY - 2023
PB - Institute of Information Theory and Automation AS CR
VL - 59
IS - 6
SP - 861
EP - 879
AB - This work presents a new approach for the imputation of missing data in weather time-series from a seasonal pattern; the seasonal time-series imputation of gap missing algorithm (STIGMA). The algorithm takes advantage from a seasonal pattern for the imputation of unknown data by averaging available data. We test the algorithm using data measured every $10$ minutes over a period of $365$ days during the year 2010; the variables include global irradiance, diffuse irradiance, ultraviolet irradiance, and temperature, arranged in a matrix of dimensions $52,560$ rows for data points over time and $4$ columns for weather variables. The particularity of this work is that the algorithm is well-suited for the imputation of values when the missing data are presented continuously and in seasonal patterns. The algorithm employs a date-time index to collect available data for the imputation of missing data, repeating the process until all missing values are calculated. The tests are performed by removing $5\%$, $10\%$, $15\%$, $20\%$, $25\%$, and $30\%$ of the available data, and the results are compared to autoregressive models. The proposed algorithm has been successfully tested with a maximum of $2,736$ contiguous missing values that account for $19$ consecutive days of a single month; this dataset is a portion of all the missing values when the time-series lacks $30\%$ of all data. The metrics to measure the performance of the algorithms are root-mean-square error (RMSE) and the coefficient of determination ($R^{2}$). The results indicate that the proposed algorithm outperforms autoregressive models while preserving the seasonal behavior of the time-series. The STIGMA is also tested with non-weather time-series of beer sales and number of air passengers per month, which also have a cyclical pattern, and the results show the precise imputation of data.
LA - eng
KW - contiguous missing values; seasonal patterns; time-series
UR - http://eudml.org/doc/299203
ER -