A classification of sequential patterns for numerical and time series multiple source data — A preliminary application on extreme weather prediction

Author(s):  
Regina Yulia Yasmin ◽  
Andi Eka Sakya ◽  
Untung Merdijanto
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham

AbstractAutomated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.


2021 ◽  
Vol 352 ◽  
pp. 109080
Author(s):  
Joram van Driel ◽  
Christian N.L. Olivers ◽  
Johannes J. Fahrenfort

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Eva Volna ◽  
Martin Kotyrba ◽  
Hashim Habiballa

The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.


2019 ◽  
Vol 31 (1) ◽  
Author(s):  
Nigel Fox ◽  
Anna Maria Jönsson

Abstract Background A warmer climate has consequences for the timing of phenological events, as temperature is a key factor controlling plant development and flowering. In this study, we analyse the effects of the long-term climate change and an extreme weather event on the first flowering day (FFD) of five spring-flowering wild plant species in the United Kingdom. Citizen science data from the UK Woodland Trust were obtained for five species: Tussilago farfara (coltsfoot), Anemone nemorosa (wood anemone), Hyacinthoides non-scripta (bluebell), Cardamine pratensis (cuckooflower) and Alliaria petiolate (garlic mustard). Results Out of the 351 site-specific time series (≥ 15-years of FFD records), 74.6% showed significant negative response rates, i.e. earlier flowering in warmer years, ranging from − 5.6 to − 7.7 days °C−1. 23.7% of the series had non-significant negative response rates, and 1.7% had non-significant positive response rates. For cuckooflower, the response rate was increasingly more negative with decreasing latitudes. The winter of 2007 reflects an extreme weather event, about 2 °C warmer compared to 2006, where the 2006 winter temperatures were similar to the 1961–1990 baseline average. The FFD of each species was compared between 2006 and 2007. The results showed that the mean FFD of all species significantly advanced between 13 and 18 days during the extreme warmer winter of 2007, confirming that FFD is affected by temperature. Conclusion Given that all species in the study significantly respond to ambient near-surface temperatures, they are suitable as climate-change indicators. However, the responses to a + 2 °C warmer winter were both more and less pronounced than expected from an analysis of ≥ 15-year time series. This may reflect non-linear responses, species-specific thresholds and cumulative temperature effects. It also indicates that knowledge on extreme weather events is needed for detailed projections of potential climate change effects.


2017 ◽  
Vol 17 (20) ◽  
pp. 12269-12302 ◽  
Author(s):  
William T. Ball ◽  
Justin Alsing ◽  
Daniel J. Mortlock ◽  
Eugene V. Rozanov ◽  
Fiona Tummon ◽  
...  

Abstract. Observations of stratospheric ozone from multiple instruments now span three decades; combining these into composite datasets allows long-term ozone trends to be estimated. Recently, several ozone composites have been published, but trends disagree by latitude and altitude, even between composites built upon the same instrument data. We confirm that the main causes of differences in decadal trend estimates lie in (i) steps in the composite time series when the instrument source data changes and (ii) artificial sub-decadal trends in the underlying instrument data. These artefacts introduce features that can alias with regressors in multiple linear regression (MLR) analysis; both can lead to inaccurate trend estimates. Here, we aim to remove these artefacts using Bayesian methods to infer the underlying ozone time series from a set of composites by building a joint-likelihood function using a Gaussian-mixture density to model outliers introduced by data artefacts, together with a data-driven prior on ozone variability that incorporates knowledge of problems during instrument operation. We apply this Bayesian self-calibration approach to stratospheric ozone in 10° bands from 60° S to 60° N and from 46 to 1 hPa (∼ 21–48 km) for 1985–2012. There are two main outcomes: (i) we independently identify and confirm many of the data problems previously identified, but which remain unaccounted for in existing composites; (ii) we construct an ozone composite, with uncertainties, that is free from most of these problems – we call this the BAyeSian Integrated and Consolidated (BASIC) composite. To analyse the new BASIC composite, we use dynamical linear modelling (DLM), which provides a more robust estimate of long-term changes through Bayesian inference than MLR. BASIC and DLM, together, provide a step forward in improving estimates of decadal trends. Our results indicate a significant recovery of ozone since 1998 in the upper stratosphere, of both northern and southern midlatitudes, in all four composites analysed, and particularly in the BASIC composite. The BASIC results also show no hemispheric difference in the recovery at midlatitudes, in contrast to an apparent feature that is present, but not consistent, in the four composites. Our overall conclusion is that it is possible to effectively combine different ozone composites and account for artefacts and drifts, and that this leads to a clear and significant result that upper stratospheric ozone levels have increased since 1998, following an earlier decline.


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