# Prediction Interval Adjustment for Load-Forecasting using Machine Learning
## 2 Literature Review  
Unlike other Data Mining (DM) algorithms such as artificial neural network (ANN) or Random Forest (RF), association rules are not so popular regarding time series prediction. One reason is that these types of algorithms are usually associated with the design of expert systems which have fallen into disuse. For instance, in a recent review of DM algorithms applied to electricity time series forecasting [8], from the more than 100 works reviewed only 6 corresponded to algorithms using rule-based prediction. Nevertheless, regarding time series prediction, in [9] they proposed to use an ensemble of forecasting algorithm which is combined using fuzzy rule-based forecasting. The purpose of the latter is to determine the best weights for each forecasting method, such that the dependence between forecasting methods and time series statistical properties is aligned. The fuzzy rules are selected using linguistic association mining given the statistical properties of the time series. Using classical time series point forecasting methods, the proposed ensemble algorithm is tested against individual and the equal-weights ensemble employing the M3 competition time series. They found that the proposed ensemble performs slightly better than the tested algorithms. In a more recent work [10], a modified _a priori_-based association-rule mining algorithm based on the Continuous Target Sequential Pattern Discovery (CTSPD) is proposed, it is then used to generate a set of association rules that help in predicting the concentration of air pollutants in New Delhi. In this work, time-dependent features from air pollutants time series are identified first to conform new variables (i.e., frequent sequences), which are then used (in the form of association rules) to predict the concentration of air pollutants. Their results showed that the proposed approach performed better than the India System of Air Quality and Weather Forecasting and Research (SAFAR). Similarly, in [11] authors propose an improved _a priori_ algorithm for temporal data mining of frequent item sets in time series. This improved algorithm is focused on reducing the computational burden of identifying all frequent item sets, by constraining temporal relations. In this sense, this algorithm determines time constraints intervals, which are then used to filter (using the time interval algebra) and mine the corresponding transactions from the database. The method is compared against the classical _a priori_ algorithm, obtaining a better performance regarding the storage and time required to mine rules. On the other hand, an approach to the analysis of the electricity demand required by home appliances is proposed in [12]. In such work, several unsupervised learning algorithms (among them association rules) are employed in the identification of appliances energy consumption patterns. Using sequential rules, authors found that there exists a heavy interdependence between the usage patterns of home appliances, the time of the use, and the user activities. In the same fashion, a more recent work related to the analysis of smart metering analysis using a Big Data infrastructure is presented in [13]. By employing unsupervised data clustering and frequent pattern mining analysis on energy time series, authors derived accurate relationships between interval-based events and appliance usages. Then, these patterns are exploited by a Bayesian Network to predict the short and long-term energy usage of home appliances. This method is then compared with Support Vector Machines (SVM) and Multi-layer Perceptron (MLP), outperforming both in all tested forecasting horizons. In general, there are many works focused on the effective estimation of prediction intervals using neural networks [14; 15; 16; 17]. Although the approach developed on this works are optimal, none of them considers the modeling of an adjustment of the prediction interval using a rule-based analysis.