In this work, a novel model for hourly PM2.5 time series imputation is proposed for the estimation of missing values in different gap sizes, including 1, 3, 6, 12, and 24 h. The proposed model is based on statistical techniques such as moving averages, linear interpolation smoothing, and linear interpolation. For the experimentation stage, two datasets were selected in Ilo City in southern Peru. Also, five benchmark models were implemented to compare the proposed model results; the benchmark models include exponential weighted moving average (EWMA), autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU). The results show that, in terms of average MAPEs, the proposed model outperforms the best deep learning model (GRU) between 26.61% and 90.69%, and the best statistical model (ARIMA) between 2.33% and 6.67%. So, the proposed model is a good alternative for the estimation of missing values in PM2.5 time series.

The imputation of time series is one of the most important tasks in the homogenization process, the quality and precision of this process will directly influence the accuracy of the time series predictions. This paper proposes two simple algorithms, but quite powerful for univariate time series imputation process, which are based on the means of the nearest neighbors for the imputation of missing data. The first of them Local Average of Neighbors Neighbors (LANN) calculates the missing value from the average of the previous neighbor and the following neighbor to the missing value. The second Local Average of Neighbors Neighbors+ (LANN+), considers a threshold parameter, which allows to differentiate the calculation of the missing values according to the difference between the neighbors: for the differences less than or equal to the threshold the missing value is calculated through of LANN and for major differences the missing value is calculated from the average of the four closest neighbors, two previous and two subsequent to the missing value. Imputation results on different time series are promising.

This paper presents a novel model for univariate time series imputation of meteorological data based on three algorithms: The first of them AHV (Average of Historical Vectors) estimates the set of NA values from historical vectors classified by seasonality; the second iNN (Interpolation to Nearest Neighbors) adjusts the curve predicted by AHV in such a way that it adequately fits to the prior and next value of the NAs gap; The third LANNf allows smoothing the curve interpolated by iNN in such a way that the accuracy of the predicted data can be improved. The results achieved by the model are very good, surpassing in several cases different algorithms with which it was compared.

This paper presents the comparison of the results of two models for the personalization of learning resources sequences in a Massive Online Open Course (MOOC). The compared models are very similar and differ just in the way how they recommend the learning resource sequences to each participant of the MOOC. In the first model, Case Based Reasoning (CBR) and Euclidean distance is used to recommend learning resource sequences that were successful in the past, while in the second model, the Q-Learning algorithm of Reinforcement Learning is used to recommend optimal learning resource sequences. The design of the learning resources is based on the flow theory considering dimensions as knowledge level of the student versus complexity level of the learning resource with the aim of avoiding the problems of anxiety or boredom during the learning process of the MOOC.

Commonly, regression for time series imputation has been implemented directly through regression models, statistical, machine learning, and deep learning techniques. In this work, a novel approach is proposed based on a classification model that determines the NA value class, and from this, two types of interpolations are implemented: polynomial or flipped polynomial. An hourly pm2.5 time series from Ilo City in southern Peru was chosen as a study case. The results obtained show that for gaps of one NA value, the proposal in most cases presents superior results to techniques such as ARIMA, LSTM, BiLSTM, GRU, and BiGRU; thus, on average, in terms of R², the proposal exceeds implemented benchmark models by between 2.4341% and 19.96%. Finally, supported by the results, it can be stated that the proposal constitutes a good alternative for short-gaps imputation in pm2.5 time series.