Search Results for author:
Found 9 papers, 6 papers with code
Fast and exact fixed-radius neighbor search based on sorting
Fixed-radius near neighbor search is a fundamental data operation that retrieves all data points within a user-specified distance to a query point.
Fast and explainable clustering based on sorting
We introduce a fast and explainable clustering method called CLASSIX.
An efficient aggregation method for the symbolic representation of temporal data
This variant utilizes a new aggregation approach tailored to the piecewise representation of time series.
Machine Learning-Based Soft Sensors for Vacuum Distillation Unit
Firstly, it is important to enhance the quality of both sets of data (laboratory measurements and physical sensors) in a data pre-processing stage (as described in Methodology section).
A comparison of LSTM and GRU networks for learning symbolic sequences
We explore the architecture of recurrent neural networks (RNNs) by studying the complexity of string sequences it is able to memorize.
Pre-treatment of outliers and anomalies in plant data: Methodology and case study of a Vacuum Distillation Unit
In this case, we used principal component analysis (PCA) with Hotelling's $T^2$ statistics to identify the long-term outliers.
ABBA: Adaptive Brownian bridge-based symbolic aggregation of time series
A new symbolic representation of time series, called ABBA, is introduced.
Algorithms for the rational approximation of matrix-valued functions
A selection of algorithms for the rational approximation of matrix-valued functions are discussed, including variants of the interpolatory AAA method, the RKFIT method based on approximate least squares fitting, vector fitting, and a method based on low-rank approximation of a block Loewner matrix.
Numerical Analysis Numerical Analysis 41A20, 65D15
Time Series Forecasting Using LSTM Networks: A Symbolic Approach
Machine learning methods trained on raw numerical time series data exhibit fundamental limitations such as a high sensitivity to the hyper parameters and even to the initialization of random weights.
