Hybridising neural network and pattern matching under dynamic time warping for time series prediction

Time series forecasting based on pattern matching has received a lot of interest in the recent years due to its simplicity and the ability to predict complex nonlinear behavior. In this paper, we investigate into the predictive potential of the method using k-NN algorithm based on R*-tree under dynamic time warping (DTW) measure. The experimental results on four real datasets showed that this approach could produce promising results in terms of prediction accuracy on time series forecasting when comparing to the similar method under Euclidean distance.

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Multi-Temporal Data Fusion in MS and SAR Images Using the Dynamic Time Warping Method for Paddy Rice Classification

Agriculture ◽

10.3390/agriculture12010077 ◽

2022 ◽

Vol 12 (1) ◽

pp. 77

Author(s):

Tsu Chiang Lei ◽

Shiuan Wan ◽

You Cheng Wu ◽

Hsin-Ping Wang ◽

Chia-Wen Hsieh

Keyword(s):

Neural Network ◽

Time Series ◽

Decision Tree ◽

Data Fusion ◽

Dynamic Time Warping ◽

Image Data ◽

Synthetic Aperture ◽

Time Warping ◽

Radar Images ◽

Dynamic Time

This study employed a data fusion method to extract the high-similarity time series feature index of a dataset through the integration of MS (Multi-Spectrum) and SAR (Synthetic Aperture Radar) images. The farmlands are divided into small pieces that consider the different behaviors of farmers for their planting contents in Taiwan. Hence, the conventional image classification process cannot produce good outcomes. The crop phenological information will be a core factor to multi-period image data. Accordingly, the study intends to resolve the previous problem by using three different SPOT6 satellite images and nine Sentinel-1A synthetic aperture radar images, which were used to calculate features such as texture and indicator information, in 2019. Considering that a Dynamic Time Warping (DTW) index (i) can integrate different image data sources, (ii) can integrate data of different lengths, and (iii) can generate information with time characteristics, this type of index can resolve certain classification problems with long-term crop classification and monitoring. More specifically, this study used the time series data analysis of DTW to produce “multi-scale time series feature similarity indicators”. We used three approaches (Support Vector Machine, Neural Network, and Decision Tree) to classify paddy patches into two groups: (a) the first group did not apply a DTW index, and (b) the second group extracted conflict predicted data from (a) to apply a DTW index. The outcomes from the second group performed better than the first group in regard to overall accuracy (OA) and kappa. Among those classifiers, the Neural Network approach had the largest improvement of OA and kappa from 89.51, 0.66 to 92.63, 0.74, respectively. The rest of the two classifiers also showed progress. The best performance of classification results was obtained from the Decision Tree of 94.71, 0.81. Observing the outcomes, the interference effects of the image were resolved successfully by various image problems using the spectral image and radar image for paddy rice classification. The overall accuracy and kappa showed improvement, and the maximum kappa was enhanced by about 8%. The classification performance was improved by considering the DTW index.

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Using dynamic time warping for quantifying effects of sinusoidal oscillation deviations during EEG time series prediction and for finding interesting EEG and fMRI changes

BMC Neuroscience ◽

10.1186/1471-2202-16-s1-p63 ◽

2015 ◽

Vol 16 (S1) ◽

Author(s):

Dinov Martin

Keyword(s):

Time Series ◽

Dynamic Time Warping ◽

Time Series Prediction ◽

Sinusoidal Oscillation ◽

Time Warping ◽

Dynamic Time

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Neural-Network Time-Series Analysis of MODIS EVI for Post-Fire Vegetation Regrowth

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7110420 ◽

2018 ◽

Vol 7 (11) ◽

pp. 420 ◽

Cited By ~ 2

Author(s):

Christos Vasilakos ◽

George E. Tsekouras ◽

Palaiologos Palaiologou ◽

Kostas Kalabokidis

Keyword(s):

Neural Network ◽

Time Series ◽

Time Series Analysis ◽

Multidimensional Scaling ◽

Fuzzy Clustering ◽

Dynamic Time Warping ◽

Time Warping ◽

Series Analysis ◽

Modis Evi ◽

Dynamic Time

The time-series analysis of multi-temporal satellite data is widely used for vegetation regrowth after a wildfire event. Comparisons between pre- and post-fire conditions are the main method used to monitor ecosystem recovery. In the present study, we estimated wildfire disturbance by comparing actual post-fire time series of Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and simulated MODIS EVI based on an artificial neural network assuming no wildfire occurrence. Then, we calculated the similarity of these responses for all sampling sites by applying a dynamic time warping technique. Finally, we applied multidimensional scaling to the warping distances and an optimal fuzzy clustering to identify unique patterns in vegetation recovery. According to the results, artificial neural networks performed adequately, while dynamic time warping and the proposed multidimensional scaling along with the optimal fuzzy clustering provided consistent results regarding vegetation response. For the first two years after the wildfire, medium-high- to high-severity burnt sites were dominated by oaks at elevations greater than 200 m, and presented a clustered (predominant) response of revegetation compared to other sites.

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New DTW Windows Type for Forward- and Backward-Lookingness Examination. Application for Inflation Expectation

Computational Economics ◽

10.1007/s10614-021-10103-y ◽

2021 ◽

Author(s):

Aleksandra Rutkowska ◽

Magdalena Szyszko

Keyword(s):

Time Series ◽

Information Content ◽

Dynamic Time Warping ◽

The Novel ◽

Time Warping ◽

Specific Question ◽

Inflation Expectation ◽

Standard Tool ◽

Dynamic Time ◽

Research Period

AbstractThis study provides an application of dynamic time warping algorithm with a new window constraint to assess consumer expectations’ information content regarding current and future inflation. Our study’s contribution is the novel application of DTW for testing expectations’ forward-lookingness. Additionally, we modify the algorithm to adjust it for a specific question on the information content of our data. The DTW overcomes constraints of the standard tool that examines forward-lookingness: DTW does not impose assumptions on time series properties. In empirical study we cover seven European counties and compare the DTW outcomes with the results of previous studies in these economies using a standard methodology. The research period covers 2001 to mid-2018. Application of DTW provides information on the degree of expectations’ forward-lookingness. The result, after standardization, are similar to the standard parameters of hybrid specification of expectations. Moreover, the rankings of most forward-looking consumers are replicated. Our results confirm the economic intuition, and they do not contradict previous studies.

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The Connections between COVID-19 and the Energy Commodities Prices: Evidence through the Dynamic Time Warping Method

Energies ◽

10.3390/en14134024 ◽

2021 ◽

Vol 14 (13) ◽

pp. 4024

Author(s):

Krzysztof Dmytrów ◽

Joanna Landmesser ◽

Beata Bieszk-Stolorz

Keyword(s):

Time Series ◽

Palm Oil ◽

Dynamic Time Warping ◽

Global Economy ◽

Price Change ◽

Commodity Prices ◽

The Other ◽

Time Warping ◽

Heating Oil ◽

Dynamic Time

The main objective of the study is to assess the similarity between the time series of energy commodity prices and the time series of daily COVID-19 cases. The COVID-19 pandemic affects all aspects of the global economy. Although this impact is multifaceted, we assess the connections between the number of COVID-19 cases and the energy commodities sector. We analyse these connections by using the Dynamic Time Warping (DTW) method. On this basis, we calculate the similarity measure—the DTW distance between the time series—and use it to group the energy commodities according to their price change. Our analysis also includes finding the time shifts between daily COVID-19 cases and commodity prices in subperiods according to the chronology of the COVID-19 pandemic. Our findings are that commodities such as ULSD, heating oil, crude oil, and gasoline are weakly associated with COVID-19. On the other hand, natural gas, palm oil, CO2 allowances, and ethanol are strongly associated with the development of the pandemic.

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