# Tri-Partition Alphabet-Based State Prediction for Multivariate Time-Series
## 2 Time-Series Prediction  
Various techniques have been proposed for predicting time-series. These methods can be categorized into the deep learning-based ones [14; 19; 20; 21; 22], matrix or tensor decomposition-based ones [23; 24], \(k\)-nearest neighbor (\(k\)NN)-based ones [15; 17; 18; 21], etc. [16; 25; 26; 27].  
For the deep learning-based ones aiming to solve the volatility problem of wind power, a forecasting model based on a convolution neural network and LightGBM was constructed by [PERSON] [14]. [PERSON] et al. proposed a deep learning-based method, namely transferred bi-directional long short-term memory model for air-quality prediction [19]. [PERSON] et al. predicted accounts' receivable cash flows by employing methods applicable to companies with many customers and many transactions [22].  
In terms of the matrix or tensor decomposition-based ones, [PERSON] et al. proposed a strategy that combines low-rank Tucker decomposition into a unified framework [48]. [PERSON] et al. proposed a deep spatial-temporal tensor factorization framework, which provides a general design for high-dimensional time-series forecasting [49]. To model the inherent rhythms and seasonality of time-series as global patterns, [PERSON] et al. [50] proposed a low-rank autoregressive tensor completion framework to model multivariate time-series' data. To generalize the effect of distance and reachability, [PERSON] et al. [51] developed an Inductive graph neural network kriging model to recover data for unsampled sensors on a network graph structure.  
For the \(k\)NN-based ones, [PERSON] et al. [15] proposed a new two-stage methodology that combines the ensemble empirical mode decomposition with a multidimensional \(k\)NN model in order to simultaneously forecast the closing price and high price of stocks. [PERSON] et al. [17] proposed an algorithm based on the kernel \(k\)NN to predict road traffic states in time-series. [PERSON] et al. [18] proposed the multivariate predicting method and discussed the prediction performance of MTS by comparing it with the univariate time-series and \(k\)NN nonparametric regression model. [PERSON] et al. [21] devised an automatic tool, i.e., a tool that works without human intervention; furthermore, the methodology should be effective and efficient. The tool can be applied to accurately forecast many time series.  
Other techniques were also used for MTS prediction. To handle multivariate long nonstationary time-series, [PERSON] et al. [16] proposed a fast prediction model based on a combination of an elastic net and a higher-order fuzzy cognitive map. [PERSON] et al. [25] proposed a weighted least squares support vector machine-based approach for univariate and multivariate time-series forecasting. To predict future outbreaks of methicillin-resistant Staphylococcus aureus, [PERSON] et al. [26] proposed the use of artificial intelligence specifically time-series forecasting techniques. The orthogonal decision tree may fail to capture the geometrical structure of data samples, so [PERSON] et al. (2017) attempted to study oblique random forests in the context of time-series forecasting.