A bidirectional weighted boundary distance algorithm for time series similarity computation based on optimized sliding window size

Cheng Peng;  ; Zhaohui Tang; Weihua Gui; Qing Chen; Jing He;

doi:10.3934/jimo.2019107

OPTIMASI PREDIKSI INFLASI DENGAN NEURAL NETWORK PADA TAHAP WINDOWING ADAKAH PENGARUH PERBEDAAN WINDOW SIZE

Technologia: Jurnal Ilmiah ◽

10.31602/tji.v12i3.5181 ◽

2021 ◽

Vol 12 (3) ◽

pp. 176

Author(s):

Rizchi Eka Wahyuni

Keyword(s):

Neural Network ◽

Time Series ◽

Window Size ◽

Sliding Window ◽

Backpropagation Neural Network ◽

Cross Sectional

AbstrakInflasi adalah indikator yang penting dalam penentuan kebijakan pemerintah. Data inflasi dirilis oleh Badan Pusat Statistik (BPS) di setiap awal bulan. Jika data inflasi dapat diprediksi lebih awal, pemerintah bisa menerapkan kebijakan yang tepat. Backpropagation neural network adalah salah satu metode prediksi yang lazim digunakan. Dengan menggunakan data bulan-bulan sebelumnya, inflasi dapat diprediksi menggunakan metode neural network dengan menggunakan teknik sliding window yang juga disebut metode windowing. Windowing adalah pembentukan struktur dari data time series menjadi data cross sectional. Ukuran dari windowing akan mempengaruhi akurasi dari hasil prediksi. Pada penelitian ini, penulis melakukan percobaan dengan tiga window size yaitu 6, 12, dan 18 untuk melihat adakah perbedaan akurasi hasil dari beberapa window size tersebut. Hasil percobaan menyimpulkan bahwa window size 6 memiliki akurasi paling baik untuk memprediksi inflasi dengan RMSE 0,435.Keywords: backpropagation, prediksi, sliding window

ФРАКТАЛЬНИЙ АНАЛІЗ КОСМІЧНИХ ЗНІМКІВ SENTINEL-2 ДЛЯ МОНІТОРИНГУ СІЛЬСЬКОГОСПОДАРСЬКИХ КУЛЬТУР

RADIOELECTRONIC AND COMPUTER SYSTEMS ◽

10.32620/reks.2020.4.03 ◽

2020 ◽

pp. 34-47

Author(s):

Максим В’ячеславович Марюшко ◽

Руслан Едуардович Пащенко

Keyword(s):

Time Series ◽

Satellite Imagery ◽

Fractal Analysis ◽

Near Infrared ◽

Fractal Dimensions ◽

Window Size ◽

Sliding Window ◽

Underlying Surface ◽

Ndvi Index ◽

Sentinel 2

The subject of the study in the article is using the new approach to the processing of spatial information from satellites for more effective and operational evaluation of crops. This is due to the growing trend of access to remote sensing data, due to the improvement of spatial and temporal resolution, which can be used in the analysis of vegetation cover and other related work. The goal of the article is the capability assessment of processing the Sentinel-2 satellite imagery using fractal dimensions to agricultural plant monitoring at different phases of the vegetative. The tasks: to research the method of constructing fractal dimensions for the Sentinel-2 satellite imagery to assess the state of crops during the vegetative phase; to assess the relationship between changes in FD averages and changes in the NDVI index of different time series remote images, to determine the advantage of calculation method fractal dimensions compared to the NDVI index. The following results were obtained. It was found that the NDVI index is most often used to quantify the state of biomass during different time intervals. But this index becomes ineffective during periods of weakening of the vegetation active phase. Accordingly, it is of practical interest to evaluate the possibility of using fractal analysis of agricultural crop satellite imagery at different vegetation phases. The basis of fractal analysis of digital images is the formation of fractal dimensions fields. The analysis of changes in the FD values on different remote images time series of the grain cornfields from the «sliding window» values is carried out. The dependences of the maximum and minimum values of FD, which are in the images, on the «window» size are investigated. It is shown that the homogeneity of the underlying surface can be estimated from the magnitude of changes in the maximum values of FD with the increasing size of the «window». It is established that the pattern of the change of the FD minimum values when changing the «window» size is due to the large sharpness of the underlying surface in the images, and the anomalous behavior of these values allows determining anomalous areas of different sizes in satellite imagery. The pattern of the change in the range of FD with increasing size of the «window», which can be used to determine the homogeneity of the underlying surface in satellite imagery, as well as during the detection of abnormal areas on them. The change analysis of FD average values with an increase in the sizes of «sliding window» is carried out. It is shown that with the same size of the «window» for different image time series, the average FD will be different, which can be used to characterize the agriculture crop vegetation phase. It is established that the pattern of changes in the FD average values is the same as the NDVI indices for different satellite imagery time series of the corn crop fields and that the magnitudes of the FD average values depend on the size of the «window». The size of the «window» is recommended, which provides accommodation between the speed of image processing and the quality of the assessment state vegetation crop. It is shown that to increase the speed of formation of the FFD during the processing of large images, it is advisable to use a «jumping window» instead of a «sliding window». It is mentioned that the «jump» value can be equal to the «window» size. This «jump» value provides maximum speed and does not affect the crop satellite imagery processing quality. Conclusions. The recommended approach to the processing of spatial data from satellites allows assessing the crops' consistency using FD. The pattern of the change in the FD mean values is identical to the NDVI change in different satellite imagery time series of corn crops. In that event, when forming the FFD, data from only one channel of the Sentinel-2 satellite can be used (for example, from the near-infrared channel – b8), and to calculate the NDVI index it is necessary to obtain data from two channels (from the near-infrared and red channels – channels b8 and b4 of the satellite Sentinel-2, respectively), which will reduce the processing time. The scale of FD average values allows detecting a qualitative change in biomass. During further research, it is advisable to perform fractal analysis of Sentinel-2 satellite imagery for other crops at different phases of the vegetation.

A Boundary Distance-Based Symbolic Aggregate Approximation Method for Time Series Data

Algorithms ◽

10.3390/a13110284 ◽

2020 ◽

Vol 13 (11) ◽

pp. 284

Author(s):

Zhenwen He ◽

Shirong Long ◽

Xiaogang Ma ◽

Hong Zhao

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Representation ◽

Series Data ◽

Financial Investment ◽

Symbolic Aggregate Approximation ◽

Wide Range ◽

Weighted Boundary ◽

Representation Method ◽

Boundary Distance

A large amount of time series data is being generated every day in a wide range of sensor application domains. The symbolic aggregate approximation (SAX) is a well-known time series representation method, which has a lower bound to Euclidean distance and may discretize continuous time series. SAX has been widely used for applications in various domains, such as mobile data management, financial investment, and shape discovery. However, the SAX representation has a limitation: Symbols are mapped from the average values of segments, but SAX does not consider the boundary distance in the segments. Different segments with similar average values may be mapped to the same symbols, and the SAX distance between them is 0. In this paper, we propose a novel representation named SAX-BD (boundary distance) by integrating the SAX distance with a weighted boundary distance. The experimental results show that SAX-BD significantly outperforms the SAX representation, ESAX representation, and SAX-TD representation.

An Improved Spatial and Temporal Reflectance Unmixing Model to Synthesize Time Series of Landsat-Like Images

Remote Sensing ◽

10.3390/rs10091388 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1388 ◽

Cited By ~ 16

Author(s):

Jianhang Ma ◽

Wenjuan Zhang ◽

Andrea Marinoni ◽

Lianru Gao ◽

Bing Zhang

Keyword(s):

Time Series ◽

Image Fusion ◽

Land Surface ◽

Temporal Change ◽

Window Size ◽

Sliding Window ◽

Spectral Variability ◽

Landsat Images ◽

Endmember Variability ◽

Unmixing Model

The trade-off between spatial and temporal resolution limits the acquisition of dense time series of Landsat images, and limits the ability to properly monitor land surface dynamics in time. Spatiotemporal image fusion methods provide a cost-efficient alternative to generate dense time series of Landsat-like images for applications that require both high spatial and temporal resolution images. The Spatial and Temporal Reflectance Unmixing Model (STRUM) is a kind of spatial-unmixing-based spatiotemporal image fusion method. The temporal change image derived by STRUM lacks spectral variability and spatial details. This study proposed an improved STRUM (ISTRUM) architecture to tackle the problem by taking spatial heterogeneity of land surface into consideration and integrating the spectral mixture analysis of Landsat images. Sensor difference and applicability with multiple Landsat and coarse-resolution image pairs (L-C pairs) are also considered in ISTRUM. Experimental results indicate the image derived by ISTRUM contains more spectral variability and spatial details when compared with the one derived by STRUM, and the accuracy of fused Landsat-like image is improved. Endmember variability and sliding-window size are factors that influence the accuracy of ISTRUM. The factors were assessed by setting them to different values. Results indicate ISTRUM is robust to endmember variability and the publicly published endmembers (Global SVD) for Landsat images could be applied. Only sliding-window size has strong influence on the accuracy of ISTRUM. In addition, ISTRUM was compared with the Spatial Temporal Data Fusion Approach (STDFA), the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), the Hybrid Color Mapping (HCM) and the Flexible Spatiotemporal DAta Fusion (FSDAF) methods. ISTRUM is superior to STDFA, slightly superior to HCM in cases when the temporal change is significant, comparable with ESTARFM and a little inferior to FSDAF. However, the computational efficiency of ISTRUM is much higher than ESTARFM and FSDAF. ISTRUM can to synthesize Landsat-like images on a global scale.

Monte Carlo Optimization for Sliding Window Size in Dixon Quality Control of Environmental Monitoring Time Series Data

Applied Sciences ◽

10.3390/app10051876 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1876

Author(s):

Zhongya Fan ◽

Huiyun Feng ◽

Jingang Jiang ◽

Changjin Zhao ◽

Ni Jiang ◽

...

Keyword(s):

Water Quality ◽

Time Series ◽

Monte Carlo ◽

Environmental Monitoring ◽

Time Series Data ◽

Window Size ◽

Sliding Window ◽

Series Data ◽

Monitoring Time

Outliers are often present in large datasets of water quality monitoring time series data. A method of combining the sliding window technique with Dixon detection criterion for the automatic detection of outliers in time series data is limited by the empirical determination of sliding window sizes. The scientific determination of the optimal sliding window size is very meaningful research work. This paper presents a new Monte Carlo Search Method (MCSM) based on random sampling to optimize the size of the sliding window, which fully takes advantage of computers and statistics. The MCSM was applied in a case study to automatic monitoring data of water quality factors in order to test its validity and usefulness. The results of comparing the accuracy and efficiency of the MCSM show that the new method in this paper is scientific and effective. The experimental results show that, at different sample sizes, the average accuracy is between 58.70% and 75.75%, and the average computation time increase is between 17.09% and 45.53%. In the era of big data in environmental monitoring, the proposed new methods can meet the required accuracy of outlier detection and improve the efficiency of calculation.

Development of A Pharmaceutical Early Warning Scoring Model for Cardiac Arrest Using Deep Learning: Retrospective Cohort Study (Preprint)

10.2196/preprints.21720 ◽

2020 ◽

Author(s):

Hsiao-Ko Chang ◽

Hui-Chih Wang ◽

Chih-Fen Huang ◽

Feipei Lai

Keyword(s):

Time Series ◽

Cardiac Arrest ◽

Early Warning ◽

Time Series Data ◽

Predictive Accuracy ◽

Vital Signs ◽

Sliding Window ◽

Early Warning Score ◽

Series Data ◽

Dynamic Time

BACKGROUND In most of Taiwan’s medical institutions, congestion is a serious problem for emergency departments. Due to a lack of beds, patients spend more time in emergency retention zones, which make it difficult to detect cardiac arrest (CA). OBJECTIVE We seek to develop a pharmaceutical early warning model to predict cardiac arrest in emergency departments via drug classification and medical expert suggestion. METHODS We propose a new early warning score model for detecting cardiac arrest via pharmaceutical classification and by using a sliding window; we apply learning-based algorithms to time-series data for a Pharmaceutical Early Warning Scoring Model (PEWSM). By treating pharmaceutical features as a dynamic time-series factor for cardiopulmonary resuscitation (CPR) patients, we increase sensitivity, reduce false alarm rates and mortality, and increase the model’s accuracy. To evaluate the proposed model we use the area under the receiver operating characteristic curve (AUROC). RESULTS Four important findings are as follows: (1) We identify the most important drug predictors: bits, and replenishers and regulators of water and electrolytes. The best AUROC of bits is 85%; that of replenishers and regulators of water and electrolytes is 86%. These two features are the most influential of the drug features in the task. (2) We verify feature selection, in which accounting for drugs improve the accuracy: In Task 1, the best AUROC of vital signs is 77%, and that of all features is 86%. In Task 2, the best AUROC of all features is 85%, which demonstrates that thus accounting for the drugs significantly affects prediction. (3) We use a better model: For traditional machine learning, this study adds a new AI technology: the long short-term memory (LSTM) model with the best time-series accuracy, comparable to the traditional random forest (RF) model; the two AUROC measures are 85%. (4) We determine whether the event can be predicted beforehand: The best classifier is still an RF model, in which the observational starting time is 4 hours before the CPR event. Although the accuracy is impaired, the predictive accuracy still reaches 70%. Therefore, we believe that CPR events can be predicted four hours before the event. CONCLUSIONS This paper uses a sliding window to account for dynamic time-series data consisting of the patient’s vital signs and drug injections. In a comparison with NEWS, we improve predictive accuracy via feature selection, which includes drugs as features. In addition, LSTM yields better performance with time-series data. The proposed PEWSM, which offers 4-hour predictions, is better than the National Early Warning Score (NEWS) in the literature. This also confirms that the doctor’s heuristic rules are consistent with the results found by machine learning algorithms.

Correction of Outliers in Temperature Time Series Based on Sliding Window Prediction in Meteorological Sensor Network

Information ◽

10.3390/info8020060 ◽

2017 ◽

Vol 8 (2) ◽

pp. 60 ◽

Cited By ~ 3

Author(s):

Li Ma ◽

Xiaodu Gu ◽

Baowei Wang

Keyword(s):

Time Series ◽

Sensor Network ◽

Sliding Window ◽

Temperature Time Series ◽

Temperature Time

EVALUATION OF THE MAIN CEOS PSEUDO CALIBRATION SITES USING MODIS BRDF/ALBEDO PRODUCTS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b1-217-2016 ◽

2016 ◽

Vol XLI-B1 ◽

pp. 217-220

Author(s):

Said Kharbouche ◽

Jan-Peter Muller

Keyword(s):

Time Series ◽

High Stability ◽

Window Size ◽

The Stability

This work describes our findings about an evaluation of the stability and the consistency of twenty primary PICSs (Pseudo-Invariant Calibration Sites). We present an analysis of 13 years of 8-daily MODIS products of BRDF parameters and white-sky-albedos (WSA) over the shortwave band. This time series of WSA and BRDFs shows the variation of the “stability” varies significantly from site to site. Using a 10x10 km window size over all the sites, the change in of WSA stability is around 4% but the isotropicity, which is an important element in inter-satellite calibration, can vary from 75% to 98%. Moreover, some PICS, especially, Libya-4 which is one of the PICS which is most employed, has significant and relatively fast changes in wintertime. PICS observations of BRDF/albedo shows that the Libya-4 PICS has the best performance but it is not too far from some sites such as Libya-1 and Mali. This study also reveals that Niger-3 PICS has the longest continuous period of high stability per year, and Sudan has the most isotropic surface. These observations have important implications for the use of these sites.

Differentially Private Autocorrelation Time-Series Data Publishing Based on Sliding Window

Security and Communication Networks ◽

10.1155/2021/6665984 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Jing Zhao ◽

Shubo Liu ◽

Xingxing Xiong ◽

Zhaohui Cai

Keyword(s):

Time Series ◽

Privacy Protection ◽

Large Scale ◽

Differential Privacy ◽

Time Series Data ◽

Sliding Window ◽

Data Publishing ◽

Series Data ◽

Data Publication ◽

Autocorrelation Time

Privacy protection is one of the major obstacles for data sharing. Time-series data have the characteristics of autocorrelation, continuity, and large scale. Current research on time-series data publication mainly ignores the correlation of time-series data and the lack of privacy protection. In this paper, we study the problem of correlated time-series data publication and propose a sliding window-based autocorrelation time-series data publication algorithm, called SW-ATS. Instead of using global sensitivity in the traditional differential privacy mechanisms, we proposed periodic sensitivity to provide a stronger degree of privacy guarantee. SW-ATS introduces a sliding window mechanism, with the correlation between the noise-adding sequence and the original time-series data guaranteed by sequence indistinguishability, to protect the privacy of the latest data. We prove that SW-ATS satisfies ε-differential privacy. Compared with the state-of-the-art algorithm, SW-ATS is superior in reducing the error rate of MAE which is about 25%, improving the utility of data, and providing stronger privacy protection.

Approximate Range Querying over Sliding Windows

Database Technologies ◽

10.4018/978-1-60566-058-5.ch121 ◽

2009 ◽

pp. 2037-2050

Author(s):

Francesco Buccafurri ◽

Gianluca Caminiti ◽

Gianluca Lax

Keyword(s):

Knowledge Discovery ◽

Data Reduction ◽

Experimental Analysis ◽

Window Size ◽

Sliding Window ◽

Knowledge Discovery In Databases ◽

Flat Structure ◽

Sliding Windows ◽

Reduction Techniques ◽

Processing Step

In the context of Knowledge Discovery in Databases, data reduction is a pre-processing step delivering succinct yet meaningful data to sequent stages. If the target of mining are data streams, then it is crucial to suitably reduce them, since often analyses on such data require multiple scans. In this chapter, we propose a histogram-based approach to reducing sliding windows supporting approximate arbitrary (i.e., non biased) range-sum queries. The histogram is based on a hierarchical structure (as opposed to the flat structure of traditional ones) and it results suitable to directly support hierarchical queries, such as drill-down and roll-up operations. In particular, both sliding window shifting and quick query answering operations are logarithmic in the sliding window size. Experimental analysis shows the superiority of our method in terms of accuracy w.r.t. the state-of-the-art approaches in the context of histogram-based sliding window reduction techniques.