scholarly journals Crop Intensity Mapping Using Dynamic Time Warping and Machine Learning from Multi-Temporal PlanetScope Data

Land ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1384
Author(s):  
Raihan Rafif ◽  
Sandiaga Swahyu Kusuma ◽  
Siti Saringatin ◽  
Giara Iman Nanda ◽  
Pramaditya Wicaksono ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dynamic Time Warping ◽  
Agricultural Land ◽  
Series Data ◽  
Time Warping ◽  
Cropping Intensity ◽  
Intensity Mapping ◽  
Intensity Information ◽  
Extreme Gradient Boosting ◽  
Dynamic Time

Crop intensity information describes the productivity and the sustainability of agricultural land. This information can be used to determine which agricultural lands should be prioritized for intensification or protection. Time-series data from remote sensing can be used to derive the crop intensity information; however, this application is limited when using medium to coarse resolution data. This study aims to use 3.7 m-PlanetScope™ Dove constellation data, which provides daily observations, to map crop intensity information for agricultural land in Magelang District, Indonesia. Two-stage histogram matching, before and after the monthly median composites, is used to normalize the PlanetScope data and to generate monthly data to map crop intensity information. Several methods including Time-Weighted Dynamic Time Warping (TWDTW) and the machine-learning algorithms: Random Forest (RF), Extremely Randomized Trees (ET), and Extreme Gradient Boosting (XGB) are employed in this study, and the results are validated using field survey data. Our results show that XGB generated the highest overall accuracy (OA) (95 ± 4%), followed by RF (92 ± 5%), ET (87 ± 6%), and TWDTW (81 ± 8%), for mapping four-classes of cropping intensity, with the near-infrared (NIR) band being the most important variable for identifying cropping intensity. This study demonstrates the potential of PlanetScope data for the production of cropping intensity maps at detailed resolutions.

scholarly journals Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

PLoS ONE ◽  
2018 ◽  
Vol 13 (5) ◽  
pp. e0197499 ◽  
Author(s):  
Yongli Liu ◽  
Jingli Chen ◽  
Shuai Wu ◽  
Zhizhong Liu ◽  
Hao Chao
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Time Series Data ◽  
Series Data ◽  
Time Warping ◽  
Dynamic Time

scholarly journals Cartographing dynamic stall with machine learning

2020 ◽  
Vol 5 (2) ◽  
pp. 819-838
Author(s):  
Matthew Lennie ◽  
Johannes Steenbuck ◽  
Bernd R. Noack ◽  
Christian Oliver Paschereit
Keyword(s):  
Machine Learning ◽  
Dynamic Time Warping ◽  
Data Science ◽  
Leading Edge ◽  
Separated Flows ◽  
Dynamic Stall ◽  
Energy Yield ◽  
Time Warping ◽  
Coherent Vortex ◽  
Dynamic Time

Abstract. Once stall has set in, lift collapses, drag increases and then both of these forces will fluctuate strongly. The result is higher fatigue loads and lower energy yield. In dynamic stall, separation first develops from the trailing edge up the leading edge. Eventually the shear layer rolls up, and then a coherent vortex forms and then sheds downstream with its low-pressure core causing a lift overshoot and moment drop. When 50+ experimental cycles of lift or pressure values are averaged, this process appears clear and coherent in flow visualizations. Unfortunately, stall is not one clean process but a broad collection of processes. This means that the analysis of separated flows should be able to detect outliers and analyze cycle-to-cycle variations. Modern data science and machine learning can be used to treat separated flows. In this study, a clustering method based on dynamic time warping is used to find different shedding behaviors. This method captures the fact that secondary and tertiary vorticity vary strongly, and in static stall with surging flow the flow can occasionally reattach. A convolutional neural network was used to extract dynamic stall vorticity convection speeds and phases from pressure data. Finally, bootstrapping was used to provide best practices regarding the number of experimental repetitions required to ensure experimental convergence.

scholarly journals Pattern Matching Trading System Based on the Dynamic Time Warping Algorithm

2018 ◽  
Author(s):  
Sang Hyuk Kim ◽  
Hee Soo Lee ◽  
Hanjun Ko ◽  
Seung Hwan Jeong ◽  
Hyun Woo Byun ◽  
...  
Keyword(s):  
Financial Markets ◽  
Real Time ◽  
Pattern Matching ◽  
Dynamic Time Warping ◽  
Series Data ◽  
Time Warping ◽  
Trading System ◽  
Index Futures ◽  
Data Movement ◽  
Dynamic Time

The futures market plays a significant role in hedging and speculating by investors. Although various models and instruments are developed for real-time trading, it is difficult to realize profit by processing and trading a vast amount of real-time data. This study proposes a real-time index futures trading strategy that uses the pattern of KOSPI 200 index futures time series data. We construct a pattern matching trading system (PMTS) based on a dynamic time warping algorithm that recognizes patterns of market data movement in the morning and determines the afternoon's clearing strategy. We adopt 13 and 27 representative patterns and conduct simulations with various ranges of parameters to find optimal ones. Our experimental results show that the PMTS provides stable and effective trading strategies with relatively low trading frequencies. Investor communities that have sustained financial markets are able to make more efficient investments by using the PMTS. In this sense, the system developed in this paper is a sustainable investment technique and helps financial markets achieve efficient sustainability.

scholarly journals Free congruence: an exploration of expanded similarity measures for time series data

2021 ◽  
Author(s):  
Lucas Cassiel Jacaruso
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Time Series Data ◽  
Similarity Measures ◽  
Distance Measures ◽  
Series Data ◽  
Time Warping ◽  
Point To Point ◽  
Dynamic Time ◽  
Point Distance

Abstract Time series similarity measures are highly relevant in a wide range of emerging applications including training machine learning models, classification, and predictive modeling. Standard similarity measures for time series most often involve point-to-point distance measures including Euclidean distance and Dynamic Time Warping. Such similarity measures fundamentally require the fluctuation of values in the time series being compared to follow a corresponding order or cadence for similarity to be established. Other existing approaches use local statistical tests to detect structural changes in time series. This paper is spurred by the exploration of a broader definition of similarity, namely one that takes into account the sheer numerical resemblance between sets of statistical properties for time series segments irrespectively of value labeling. Further, the presence of common pattern components between time series segments was examined even if they occur in a permuted order, which would not necessarily satisfy the criteria of more conventional point-to-point distance measures. The newly defined similarity measures were tested on time series data representing over 20 years of cooperation intent expressed in global media sentiment. Tests determined whether the newly defined similarity measures would accurately identify stronger resemblance, on average, for pairings of similar time series segments (exhibiting overall decline) than pairings of differing segments (exhibiting overall decline and overall rise). The ability to identify patterns other than the obvious overall rise or decline that can accurately relate samples is regarded as a first step towards assessing the value of the newly explored similarity measures for classification or prediction. Results were compared with those of Dynamic Time Warping on the same data for context. Surprisingly, the test for numerical resemblance between sets of statistical properties established stronger resemblance for pairings of decline years with greater statistical significance than Dynamic Time Warping on the particular data and sample size used.

scholarly journals DESIGNING TRANSLATION TOOL: BETWEEN SIGN LANGUAGE TO SPOKEN TEXT ON KINECT TIME SERIES DATA USING DYNAMIC TIME WARPING

SINERGI ◽  
2018 ◽  
Vol 22 (2) ◽  
pp. 91
Author(s):  
Zico Pratama Putera ◽  
Mila Desi Anasanti ◽  
Bagus Priambodo
Keyword(s):  
User Interface ◽  
Sign Language ◽  
Dynamic Time Warping ◽  
User Study ◽  
Hearing Impaired ◽  
Microsoft Kinect ◽  
Series Data ◽  
Time Warping ◽  
Dynamic Gestures ◽  
Dynamic Time

The gesture is one of the most natural and expressive methods for the hearing impaired. Most researchers, however, focus on either static gestures, postures or a small group of dynamic gestures due to the complexity of dynamic gestures. We propose the Kinect Translation Tool to recognize the user's gesture. As a result, the Kinect Translation Tool can be used for bilateral communication with the deaf community. Since real-time detection of a large number of dynamic gestures is taken into account, some efficient algorithms and models are required. The dynamic time warping algorithm is used here to detect and translate the gesture. Kinect Sign Language should translate sign language into written and spoken words. Conversely, people can reply directly with their spoken word, which is converted into literal text together with the animated 3D sign language gestures. The user study, which included several prototypes of the user interface, was carried out with the observation of ten participants who had to gesture and spell the phrases in American Sign Language (ASL). The speech recognition tests for simple phrases have therefore shown good results. The system also recognized the participant's gesture very well during the test. The study suggested that a natural user interface with Microsoft Kinect could be interpreted as a sign language translator for the hearing impaired.

Segmented Dynamic Time Warping

2019 ◽  
pp. 1-19
Author(s):  
Ruizhe Ma ◽  
Azim Ahmadzadeh ◽  
Soukaina Filali Boubrahimi ◽  
Rafal A Angryk
Keyword(s):  
Dynamic Time Warping ◽  
Time Series Data ◽  
Series Data ◽  
Time Warping ◽  
Use Of Time ◽  
Computational Speed ◽  
Selection Parameters ◽  
And Performance ◽  
Dynamic Time ◽  
The Relationship

Initially used in speech recognition, the dynamic time warping algorithm (DTW) has regained popularity with the widespread use of time series data. While demonstrating good performance, this elastic measure has two significant drawbacks: high computational costs and the possibility of pathological warping paths. Due to the balance between performance and the tightness of restrictions, the effects of many improvement techniques are either limited in effect or use accuracy as a trade-off. In this chapter, the authors discuss segmented-DTW (segDTW) and its applications. The intuition behind significant features is first established. Then considering the variability of different datasets, the relationship between specific global feature selection parameters, feature numbers, and performance are demonstrated. Other than the improvement in computational speed and scalability, another advantage of segDTW is that while it can be a stand-alone heuristic, it can also be easily combined with other DTW improvement methods.

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