| Title:
|
Seasonal time-series imputation of gap missing algorithm (STIGMA) (English) |
| Author:
|
Rangel-Heras, Eduardo |
| Author:
|
Zuniga, Pavel |
| Author:
|
Alanis, Alma Y. |
| Author:
|
Hernandez-Vargas, Esteban A. |
| Author:
|
Sanchez, Oscar D. |
| Language:
|
English |
| Journal:
|
Kybernetika |
| ISSN:
|
0023-5954 (print) |
| ISSN:
|
1805-949X (online) |
| Volume:
|
59 |
| Issue:
|
6 |
| Year:
|
2023 |
| Pages:
|
861-879 |
| Summary lang:
|
English |
| . |
| Category:
|
math |
| . |
| Summary:
|
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. (English) |
| Keyword:
|
contiguous missing values |
| Keyword:
|
seasonal patterns |
| Keyword:
|
time-series |
| MSC:
|
62-04 |
| MSC:
|
68Pxx |
| idZBL:
|
Zbl 07830568 |
| DOI:
|
10.14736/kyb-2023-6-0861 |
| . |
| Date available:
|
2024-02-26T11:11:44Z |
| Last updated:
|
2024-08-02 |
| Stable URL:
|
http://hdl.handle.net/10338.dmlcz/152261 |
| . |
| Reference:
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