Time series classificationPairwise shapeletsRandom forestDecomposed mean decrease impurityShapelet is a discriminative subsequence of time series. An advanced shapelet-based method is to embed shapelet into the accurate and fast random forest. However, there are several limitations. First, random shapelet ...
Another common approach for multivariate time series classification is by applying dimensional reduction techniques or by concatenating all dimensions of a multivariate time series into a univariate time series. Symbolic Representation for Multivariate Time Series (SMTS) [22] applies a random forest on t...
The Echo state network (ESN) is an efficient recurrent neural network that has achieved good results in time series prediction tasks. Still, its applicatio
Logistic Regression: A simple yet effective model for binary classification tasks. In scikit-learn, this can be implemented using LogisticRegression. This model is particularly useful for datasets with linear decision boundaries. Random Forest: Offers robust performance by combining multiple decision trees...
Time series classification has been an important research topic in data science for the past decades with constantly improving methods such as the state-of-the-art Generalized Random Shapelet Forests (gRSF) algorithm. Novel approaches are largely motivated by real-world applications that often require...
Classification results were obtained by the random forest classifiers, and the input features consisted of the spectral channels, spectral index, time-series and phenological information, elevation, and slope calculated from the digital elevation model (DEM). Please notice that the classifiers as well ...
time series for three study areas in Texas, Kansas and South Dakota that have different amounts of missing data and land cover complexity. A series of random forest classifications were conducted on the refined LE DR bands using varying proportions of training data provided by the United States ...
2.2.2. Savitzky-Golay filter After envelope detection, the random noise, presented within the envelope nodes set prevents the assumption that the NDVI time-series are smooth from being met; thus, an SG filter to further Figure 2. Conceptual scheme of the proposed ED-SG method. 558 X. LIU ...
We show that none of the previously evaluated regressors can outperform a regression adaptation of a standard classifier, rotation forest. We introduce two new TSER algorithms developed from related work in time series classification. FreshPRINCE is a pipeline estimator consisting of a transform into ...
Time series classification has received great attention over the past decade with a wide range of methods focusing on predictive performance by exploiting