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\
contiguous missing values, seasonal patterns, time-series
62-04, 68Pxx