A Weighted DTW Approach for Similarity Matching over Uncertain Time Series

Zuo, Liangli; Li, Yan

doi:10.20532/cit.2018.1004217

Cited by 3 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To simplify the representation and processing of uncertain data, a pdf is also simply characterized by the mean and variance in some application scenarios. Such a representation of uncertain data has been applied in uncertain time series (e.g., [1]). Following this step, in this paper, we adopt the pdf with a form of mean and variance as the underlying distributions of uncertain values in uncertain time series.…”

Section: Related Workmentioning

confidence: 99%

“…Uncertain time series (UTS) consists of observations at fixed, equally spaced time points, in which the value at each time point is uncertain [1].…”

Section: Definition 2 (Uncertain Time Series)mentioning

confidence: 99%

“…Here we have v i = (t i , u i , p j ), in which t i denotes the ith sampling time point, u i denotes the observation value at t i , and p i represents the probability density at t i with the form of f ( μ j , σ j ) [1].…”

Section: Definition 2 (Uncertain Time Series)mentioning

confidence: 99%

“…A time series is a sequence of ordered and equally spaced fixed values [1]. Time series data extensively exist in many real-world applications.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Shapelet Transform Classification over Uncertain Time Series

Zuo

2020

CIT. J. comput. inf. technol

View full text Add to dashboard Cite

A shapelet is a time-series subsequence that can represent local, phase-independent similarity in shape. Time series classification with subsequences can save computing cost, improve computing speed and improve algorithm accuracy. The shapelet-based approaches for time series classification have an advantage of interpretability. Concentrating on uncertain time series, this paper tries to apply the shapelet-based method to classify uncertain time series. Due to the high dimensions of time series, the number of the generated candidate shapelets is generally huge. As a result, the calculation amount is large too. To deal with this problem, in this paper, we introduce a piecewise linear representation (PLR) method for uncertain time series based on key points so that the traditional shapelet discovery algorithm can be improved efficiently. We verify our approach with experiments. The experimental results show that the proposed shapelet algorithm can be used for uncertain time series and it can provide classification accuracy well while reducing time cost.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Uncertain time series (UTS) consists of observations at fixed, equally spaced time points, in which the value at each time point is uncertain [1].…”

Section: Definition 2 (Uncertain Time Series)mentioning

confidence: 99%

Section: Definition 2 (Uncertain Time Series)mentioning

confidence: 99%

“…A time series is a sequence of ordered and equally spaced fixed values [1]. Time series data extensively exist in many real-world applications.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Shapelet Transform Classification over Uncertain Time Series

Zuo

2020

CIT. J. comput. inf. technol

View full text Add to dashboard Cite

show abstract

SCALING THE ¹⁴C-EXCURSION SIGNAL IN MULTIPLE TREE-RING SERIES WITH DYNAMIC TIME WARPING

et al. 2022

View full text Add to dashboard Cite

A signal of rapid changes in 14C production is logged in annual series of 14C derived from tree rings, which can be associated with diverse effects of cosmic-ray fluxes, including solar burst and supernova events. These 14C signatures may vary in time and space. The intensity and structure of the 14C signal is multifaced, which complicates understanding of the forcing and attribution of the underlying astrophysical events. It was suggested that Δ14C in 1052/53 CE and 1054/55 CE signatures at a 4‰–6‰ range over two years could be caused by the Crab Nebula supernova (SN1054) or/and solar perturbation. The temporal incoherence of the signals in published 14C series is investigated with dynamic time warping (DTW), novel approach for matching time-behavioral patterns in multiple 14C datasets. DTW analysis of four 14C signatures from tree rings of California, Finland and England suggests that 14C spikes between 1052 CE and 1055 CE can be caused by a single event. The flickering fingerprint may result from cross-dating inconformity. Cross-checking of tree-ring records from distant locations is impossible sometimes due to large difference in environmental conditions limiting tree growth. The methodology helps to align the signals and can be applied to other 14C datasets.

show abstract

Effects of wind speed and wind direction on crop yield forecasting using dynamic time warping and an ensembled learning model

Bediako-Kyeremeh,

Ma,

Rong

et al. 2024

PeerJ

View full text Add to dashboard Cite

The cultivation of cashew crops carries numerous economic advantages, and countries worldwide that produce this crop face a high demand. The effects of wind speed and wind direction on crop yield prediction using proficient deep learning algorithms are less emphasized or researched. We propose a combination of advanced deep learning techniques, specifically focusing on long short-term memory (LSTM) and random forest models. We intend to enhance this ensemble model using dynamic time warping (DTW) to assess the spatiotemporal data (wind speed and wind direction) similarities within Jaman North, Jaman South, and Wenchi with their respective production yield. In the Bono region of Ghana, these three areas are crucial for cashew production. The LSTM-DTW-RF model with wind speed and wind direction achieved an R2 score of 0.847 and the LSTM-RF model without these two key features R2 score of (0.74). Both models were evaluated using the augmented Dickey-Fuller (ADF) test, which is commonly used in time series analysis to assess stationarity, where the LSTM-DTW-RF achieved a 90% level of confidence, while LSTM-RF attained an 87.99% level. Among the three municipalities, Jaman South had the highest evaluation scores for the model, with an RMSE of 0.883, an R2 of 0.835, and an MBE of 0.212 when comparing actual and predicted values for Wenchi. In terms of the annual average wind direction, Jaman North recorded (270.5 SW°), Jaman South recorded (274.8 SW°), and Wenchi recorded (272.6 SW°). The DTW similarity distance for the annual average wind speed across these regions fell within specific ranges: Jaman North (±25.72), Jaman South (±25.89), and Wenchi (±26.04). Following the DTW similarity evaluation, Jaman North demonstrated superior performance in wind speed, while Wenchi excelled in wind direction. This underscores the potential efficiency of DTW when incorporated into the analysis of environmental factors affecting crop yields, given its invariant nature. The results obtained can guide further exploration of DTW variations in combination with other machine learning models to predict higher cashew yields. Additionally, these findings emphasize the significance of wind speed and direction in vertical farming, contributing to informed decisions for sustainable agricultural growth and development.

show abstract

A Weighted DTW Approach for Similarity Matching over Uncertain Time Series

Cited by 3 publications

References 21 publications

A Shapelet Transform Classification over Uncertain Time Series

A Shapelet Transform Classification over Uncertain Time Series

SCALING THE ¹⁴C-EXCURSION SIGNAL IN MULTIPLE TREE-RING SERIES WITH DYNAMIC TIME WARPING

Effects of wind speed and wind direction on crop yield forecasting using dynamic time warping and an ensembled learning model

Contact Info

Product

Resources

About

A Weighted DTW Approach for Similarity Matching over Uncertain Time Series

Cited by 3 publications

References 21 publications

A Shapelet Transform Classification over Uncertain Time Series

A Shapelet Transform Classification over Uncertain Time Series

SCALING THE 14C-EXCURSION SIGNAL IN MULTIPLE TREE-RING SERIES WITH DYNAMIC TIME WARPING

Effects of wind speed and wind direction on crop yield forecasting using dynamic time warping and an ensembled learning model

Contact Info

Product

Resources

About

SCALING THE ¹⁴C-EXCURSION SIGNAL IN MULTIPLE TREE-RING SERIES WITH DYNAMIC TIME WARPING