Waved zero streaming: A time series compression framework with adaptive segmentation optimization

An adaptive multi-objective joint optimization framework for marine hybrid energy storage system design considering energy management strategy

An efficient copy move forgery detection using adaptive watershed segmentation with AGSO and hybrid feature extraction

A shape-based adaptive segmentation of time-series using particle swarm optimization

We present our initial findings regarding the problem of the impact that time series compression may have on similarity-queries, in the settings in which the elements of the dataset are accompanied with additional contexts. Broadly, the main objective of any data compression approach is to provide a more compact (i.e., smaller size) representation of a given original dataset. However, as has been observed in the large body of works on compression of spatial data, applying a particular algorithm “blindly” may yield outcomes that defy the intuitive expectations – e.g., distorting certain topological relationships that exist in the “raw” data [7]. In this study, we quantify this distortion by defining a measure of similarity distortion based on Kendall’s \(\tau \). We evaluate this measure, and the correspondingly achieved compression ratio for the five most commonly used time series compression algorithms and the three most common time series similarity measures. We report some of our observations here, along with the discussion of the possible broader impacts and the challenges that we plan to address in the future.

Successful product development depends on many factors. Among the most important factors are identification and satisfaction of customers’ perceived needs, the accessibility, size and growth rate of the target market, and, of course, production costs. Since the early 1970s marketing researchers achieved remarkable results in developing methods to measure consumer preferences of multiattributed products. Additionally, market segmentation methods have been an important issue in strategic marketing research. This study, however, concentrates on a new method of product design optimization. It is shown how genetic algorithms are used to simultaneously discover optimal multi-attributed products for different customer preferences. For that purpose we chose an interactive version of the genetic algorithm where genetic operators like selection, mutation and crossover are applied as usual. The use of the interactive genetic algorithm is most suitable, where measures of utility are difficult or impossible to specify mathematically. Imprecise optimization in terms of a priori unknown individual consumer decision rules and preferences is an important issue for marketing researchers. The interactive genetic algorithm tries to solve design problems in that the consumer plays the role of the objective function during data collection.

Power quality time series data mining using S-transform and fuzzy expert system

Data compression is a key technique for reducing the cost of data transfer from storage to compute nodes. Increasingly, modern data scales necessitate lossy compression techniques, where exactness is sacrificed for a smaller compressed representation. One challenge in lossy compression is that different applications may have different accuracy demands. Today's compression techniques struggle in this setting either forcing the user to compress at the strictest accuracy demand, or to re-encode the data at multiple resolutions. This paper proposes a simple, but effective multiresolution compression algorithm for time series data, where a single encoding can effectively be decompressed at multiple output resolutions. There are a number of benefits over current state-of-the-art techniques for time series compression. (1) The storage footprint of this encoding is smaller than re-encoding the data at multiple resolutions. (2) Similarly, the compression latency is generally smaller than re-encoding at multiple resolutions. (3) Finally, the decompression latency of our encoding is significantly faster than single encodings at the strictest accuracy demand.

Time series compression encodes the information in a time-ordered sequence of data points into fewer bits, thereby reducing storage costs and possibly other costs. Compression methods are either general or XOR-based. General compression methods are time-consuming and are not suitable in streaming scenarios, while XOR-based methods are unable to consistently maintain high compression ratios. Further, existing methods compress the integer and decimal parts of floating-point values as a whole, thus disregarding the different characteristics of the two parts. We propose Camel , a new compression method for floating-point time series with the goal of advancing the compression ratios and efficiency achievable. Camel compresses the integer and decimal parts of the double-precision floating-point numbers in time series separately; and instead of performing XOR operations on values using their previous value, Camel identifies values that enable higher compression ratios. Camel also includes means of indexing compressed data, thereby making it possible to query compressed data efficiently. We report on an empirical study of Camel and 11 lossless and 6 lossy compression methods on 22 public datasets and three industrial datasets from AliCloud. The study offers evidence that Camel is capable of outperforming existing methods in terms of both compression ratio and efficiency and is capable of excellent compression performance on both time series and non-time series data.

Fuzzy Dynamic Parameter Adaptation for Particle Swarm Optimization of Modular Granular Neural Networks Applied to Time Series Prediction

We formalise the notion of important extrema of a time series, that is, its major minima and maxima; analyse the basic mathematical properties of important extrema; and apply these results to the problem of time-series compression. First, we define the numeric importance levels of extrema in a series, and present algorithms for identifying major extrema and computing their importances. Then, we give a procedure for fast lossy compression of a time series at a given rate, by extracting its most important minima and maxima, and discarding the other points.

Short-term multivariate time series load data forecasting at low-voltage level using optimised deep-ensemble learning-based models

A multi-scale relevance vector regression approach for daily urban water demand forecasting

The REACTIV algorithm has emerged as a powerful tool for detecting changes in large-scale Sentinel-1 SAR time series, providing an intuitive visualization of temporal dynamics. Since its introduction, it has been widely adopted in various Earth observation applications, ranging from maritime surveillance to urban expansion monitoring. This article reviews its main applications within the remote sensing community and evaluates its contributions to SAR-based change detection. Despite its success, the algorithm in its generic form has limitations in specific contexts. To address these, we introduce three advancements: (1) an event-based alert system for detecting abrupt changes, demonstrated on refugee camp monitoring; (2) a seasonal adaptation for filtering cyclic variations, particularly useful in glacier monitoring ; (3) a polarimetric extension leveraging multivariate techniques for detecting weak signals, such as RFI and concealed vessels. These developments enhance REACTIV’s versatility, making it a more sensitive and interpretable tool for both environmental and anthropogenic change monitoring.

This study presents a dynamic equivalent method of wind farms with permanent magnetic synchronous generator (PMSG) wind turbines for power system stability analysis. First, a novel clustering indicator is set up based on the morphology similarity distance and cosine similarity between trajectories with the measurable output characteristics time series of wind turbines, and the K‐means algorithm is applied to cluster the PMSGs. Combining silhouette coefficient, the robustness analysis of the clustering method under different scenarios is adopted. In each cluster, a trajectory sensitivity based key parameters selection is introduced to reduce the control parameters to be identified. Moreover, a multi‐objective adaptive function is established and the multi‐controllers aggregated parameters are optimised by adaptive evolutionary particle swarm optimisation algorithm. Thus, a wind farm model represented by multi equivalent wind turbines is obtained. The IEEE 39‐bus system with a PMSG‐based wind farm is used as test system to verify the validity of the proposed dynamic equivalent approach in time domain and frequency domain, respectively. It has been demonstrated that the proposed model can reduce the model complexity and improve the calculation efficiency of wind farms.

Due to the rapid population growth and economic development, water environmental protection pressures has been increasing recently. This paper focuses on the pollution of water quality, building a water quality assessment model to analyze the water quality level, and makes an objective further prediction of the trend of its factors. In this paper, the mutation factor of genetic algorithm is introduced into the PSO algorithm. The Least Squares Support Vector Machine (LS-SVM) based on adaptive Particle Swarm Optimization (PSO) algorithm used to optimize the hyper-parameter builds one water quality classification assessment model. The fuzzy information granulation method is combined with the Least Square Support Regression (LS-SVR) to set up a water quality time series model, which can predict the trend of changes in water quality data in three days. With the help of the theoretical analysis and experimental data, this assessment model and the prediction algorithm are faster in training speed and higher in accuracy, compared with the traditional BP neural network.