Adaptive Normalization: A novel data normalization approach for non-stationary time series

Ogasawara, Eduardo; Martinez, Leonardo Conegundes; Oliveira, Daniel de; Zimbrão, Geraldo; Pappa, Gisele L.; Mattoso, Marta

doi:10.1109/ijcnn.2010.5596746

Cited by 118 publications

(80 citation statements)

References 15 publications

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“…After modeling, neural network responses are converted back from normalized to natural range of values. The process of normalization has been detailed in the works of a number of scientists [12,14,15,16].…”

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confidence: 99%

Neural network method as means of processing experimental data on grain crop yields

et al. 2020

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In the work based on agroecological and technological testing of varieties of grain crops of domestic and foreign breeding, winter triticale in particular, conducted on the experimental field of the Smolensk State Agricultural Academy between 2015 and 2019, we present the methodology and results of processing the experimental data used for constructing the neural network model. Neural networks are applicable for solving tasks that are difficult for computers of traditional design and humans alike. Those are processing large volumes of experimental data, automation of image recognition, approximation of functions and prognosis. Neural networks include analyzing subject areas and weight coefficients of neurons, detecting conflict samples and outliers, normalizing data, determining the number of samples required for teaching a neural network and increasing the learning quality when their number is insufficient, as well as selecting the neural network type and decomposition based on the number of input neurons. We consider the technology of initial data processing and selecting the optimal neural network structure that allows to significantly reduce modeling errors in comparison with neural networks created with unprepared source data. Our accumulated experience of working with neural networks has demonstrated encouraging results, which indicates the prospects of this area, especially when describing processes with large amounts of variables. In order to verify the resulting neural network model, we have carried out a computational experiment, which showed the possibility of applying scientific results in practice.

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confidence: 99%

Neural network method as means of processing experimental data on grain crop yields

et al. 2020

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“…current), meani-1 is the mean of (i-1) th window (i.e. previous), if it exceeds threshold then reference min-max values are updated with new min-max (Lines 08-09), otherwise reference min-max values are incrementally updated with every new window and used to normalize current window elements (Lines [11][12][13][14]. This entire process repeats until all data points are normalized (Line 05-17).…”

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confidence: 99%

Adaptive Normalization in Streaming Data

Gupta

Hewett

2019

Proceedings of the 2019 3rd International Conference on Big Data Research

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In today's digital era, data are everywhere from Internet of Things to health care or financial applications. This leads to potentially unbounded ever-growing Big data streams and it needs to be utilized effectively. Data normalization is an important preprocessing technique for data analytics. It helps prevent mismodeling and reduce the complexity inherent in the data especially for data integrated from multiple sources and contexts. Normalization of Big Data stream is challenging because of evolving inconsistencies, time and memory constraints, and non-availability of whole data beforehand. This paper proposes a distributed approach to adaptive normalization for Big data stream. Using sliding windows of fixed size, it provides a simple mechanism to adapt the statistics for normalizing changing data in each window. Implemented on Apache Storm, a distributed real-time stream data framework, our approach exploits distributed data processing for efficient normalization. Unlike other existing adaptive approaches that normalize data for a specific use (e.g., classification), ours does not. Moreover, our adaptive mechanism allows flexible controls, via user-specified thresholds, for normalization tradeoffs between time and precision. The paper illustrates our proposed approach along with a few other techniques and experiments on both synthesized and real-world data. The normalized data obtained from our proposed approach, on 160,000 instances of data stream, improves over the baseline by 89% with 0.0041 root-mean-square error compared with the actual data.

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“…After min-max normalization, 30 we applied discriminating statistics. The discriminating statistics is some quantity that can be computed both from the measured time series and also from a time series that is consistent with the null hypothesis.…”

Section: Proposed Approach For Analysismentioning

confidence: 99%

IMF-based chaotic characterization of AP and ML visually-driven postural responses

2013

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The objective was to analyze visually driven postural responses and characterize any non-linear behaviour. We recorded physiological responses for two adults, 260 trials each. The subjects maintained quite stance while fixating for four seconds within an immersive room, EON Icube, where the reference to the visual stimuli, i.e., the virtual platform, randomly oscillated in Gaussian orientation 90 0 and 270 0 for antero-posterior (AP), and, 0 0 and 180 0 for medio-lateral (ML) at three different frequencies (0.125, 0.25, and 0.5 Hz). We accomplished stationary derivatives of posture time series by taking the intrinsic mode functions (IMFs). The phase space plot of IMF shows evidence of the existence of non-linear attractors in both ML and AP. Correlation integral slope with increasing embedding dimension is similar to random white noise for ML, and similar to non-linear chaotic series for AP. Next, recurrence plots indicate the existence of more non-linearity for AP than that for ML. The patterns of the dots after 200 th time stamp (near onset) appears to be aperodic in AP. At higher temporal windows, AP entropy tends more toward chaotic series, than that of ML. There are stronger non-linear components in AP than that in ML regardless of the speed conditions.

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Adaptive Normalization: A novel data normalization approach for non-stationary time series

Cited by 118 publications

References 15 publications

Neural network method as means of processing experimental data on grain crop yields

Neural network method as means of processing experimental data on grain crop yields

Adaptive Normalization in Streaming Data

IMF-based chaotic characterization of AP and ML visually-driven postural responses

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