{'fraction_non_alphanumeric': 0.02448210922787194, 'fraction_numerical': 0.011299435028248588, 'mean_word_length': 4.87292817679558, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
K-Nearest Temperature Trends: A Method for Weather Temperature Data Imputation

In this paper, we describe a novel method for imputing weather temperature data. The technique discussed is targeted toward unit imputation of a missing surface temperature value, for a specific weather station, on a specific date. The imputation method relies solely on the available daily maximum temperature data set of the weather station. We propose a hybrid approach, K-Nearest Temperature Trends (KNTT) which identifies a cluster of K years, showing nearest temperature trends to that of the year of the missing value date. Next, the missing temperature value of the date is imputed by taking average of the values for the same date of the identified K-Trends years. We used the data set of temperature values from 38 weather stations of Pakistan, spanning over 30 years (1980-2010), for our experiments. We evaluated our methodology by using ME, MAE and RMSE and the results show that our technique imputed correctly, with an error rate less than the standard KNN technique.