scholarly journals Practical methods of tracking of nonstationary time series applied to real-world data

1996 ◽  
Author(s):  
Ian T. Nabney ◽  
Alan McLachlan ◽  
David Lowe
Keyword(s):  
Time Series ◽  
Real World ◽  
Nonstationary Time Series ◽  
Real World Data ◽  
World Data

scholarly journals Establishment of a terminal prognosis prediction model by applying time series analysis to real-world data

2015 ◽  
Vol 26 ◽  
pp. vii99 ◽  
Author(s):  
Yu Uneno ◽  
Kei Taneishi ◽  
Masashi Kanai ◽  
Akiko Tamon ◽  
Kazuya Okamoto ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Prediction Model ◽  
Real World ◽  
Real World Data ◽  
World Data ◽  
Prognosis Prediction ◽  
Series Analysis ◽  
Prognosis Prediction Model ◽  
Terminal Prognosis

scholarly journals DFT21: Discrete Fourier Transform in the 21st century

2021 ◽  
Author(s):  
Prasanta Pal ◽  
Shataneek Banerjee ◽  
Amardip Ghosh ◽  
David R. Vago ◽  
Judson Brewer
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Frequency Domain ◽  
Discrete Fourier Transform ◽  
Real World ◽  
Data Science ◽  
Digital Data ◽  
Series Data ◽  
Real World Data ◽  
World Data

<div> <div> <div> <p>Knowingly or unknowingly, digital-data is an integral part of our day-to-day lives. Realistically, there is probably not a single day when we do not encounter some form of digital-data. Typically, data originates from diverse sources in various formats out of which time-series is a special kind of data that captures the information about the time-evolution of a system under observation. How- ever, capturing the temporal-information in the context of data-analysis is a highly non-trivial challenge. Discrete Fourier-Transform is one of the most widely used methods that capture the very essence of time-series data. While this nearly 200-year-old mathematical transform, survived the test of time, however, the nature of real-world data sources violates some of the intrinsic properties presumed to be present to be able to be processed by DFT. Adhoc noise and outliers fundamentally alter the true signature of the frequency domain behavior of the signal of interest and as a result, the frequency-domain representation gets corrupted as well. We demonstrate that the application of traditional digital filters as is, may not often reveal an accurate description of the pristine time-series characteristics of the system under study. In this work, we analyze the issues of DFT with real-world data as well as propose a method to address it by taking advantage of insights from modern data-science techniques and particularly our previous work SOCKS. Our results reveal that a dramatic, never-before-seen improvement is possible by re-imagining DFT in the context of real-world data with appropriate curation protocols. We argue that our proposed transformation DFT21 would revolutionize the digital world in terms of accuracy, reliability, and information retrievability from raw-data. </p> </div> </div> </div>

scholarly journals DFT21: Discrete Fourier Transform in the 21st century

2021 ◽  
Author(s):  
Prasanta Pal ◽  
Shataneek Banerjee ◽  
Amardip Ghosh ◽  
David R. Vago ◽  
Judson Brewer
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Frequency Domain ◽  
Discrete Fourier Transform ◽  
Real World ◽  
Data Science ◽  
Digital Data ◽  
Series Data ◽  
Real World Data ◽  
World Data

<div> <div> <div> <p>Knowingly or unknowingly, digital-data is an integral part of our day-to-day lives. Realistically, there is probably not a single day when we do not encounter some form of digital-data. Typically, data originates from diverse sources in various formats out of which time-series is a special kind of data that captures the information about the time-evolution of a system under observation. How- ever, capturing the temporal-information in the context of data-analysis is a highly non-trivial challenge. Discrete Fourier-Transform is one of the most widely used methods that capture the very essence of time-series data. While this nearly 200-year-old mathematical transform, survived the test of time, however, the nature of real-world data sources violates some of the intrinsic properties presumed to be present to be able to be processed by DFT. Adhoc noise and outliers fundamentally alter the true signature of the frequency domain behavior of the signal of interest and as a result, the frequency-domain representation gets corrupted as well. We demonstrate that the application of traditional digital filters as is, may not often reveal an accurate description of the pristine time-series characteristics of the system under study. In this work, we analyze the issues of DFT with real-world data as well as propose a method to address it by taking advantage of insights from modern data-science techniques and particularly our previous work SOCKS. Our results reveal that a dramatic, never-before-seen improvement is possible by re-imagining DFT in the context of real-world data with appropriate curation protocols. We argue that our proposed transformation DFT21 would revolutionize the digital world in terms of accuracy, reliability, and information retrievability from raw-data. </p> </div> </div> </div>

scholarly journals Show Me What You’re Looking For

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Leonid Schwenke ◽  
Martin Atzmueller
Keyword(s):  
Time Series ◽  
Real World ◽  
Specific Form ◽  
Learning Performance ◽  
Complex Data ◽  
Real World Data ◽  
World Data

While Transformers have shown their advantages consideringtheir learning performance, their lack of explainabilityand interpretability is still a major problem.This specifically relates to the processing of time series,as a specific form of complex data. In this paper,we propose an approach for visualizing abstracted informationin order to enable computational sensemakingand local interpretability on the respective Transformermodel. Our results demonstrate the efficacy ofthe proposed abstraction method and visualization, utilizingboth synthetic and real world data for evaluation.

scholarly journals Compression-Based Methods of Time Series Forecasting

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 284
Author(s):  
Konstantin Chirikhin ◽  
Boris Ryabko
Keyword(s):  
Time Series ◽  
Real World ◽  
Social Indicators ◽  
Computation Time ◽  
Time Series Forecasting ◽  
Real World Data ◽  
World Data ◽  
Practical Applications ◽  
Multiple Data ◽  
Universal Codes

Time series forecasting is an important research topic with many practical applications. As shown earlier, the problems of lossless data compression and prediction are very similar mathematically. In this article, we propose several forecasting methods based on real-world data compressors. We consider predicting univariate and multivariate data, describe how multiple data compressors can be combined into one forecasting method with automatic selection of the best algorithm for the input data. The developed forecasting techniques are not inferior to the known ones. We also propose a way to reduce the computation time of the combined method by using the so-called time-universal codes. To test the proposed techniques, we make predictions for real-world data such as sunspot numbers and some social indicators of Novosibirsk region, Russia. The results of our computations show that the described methods find non-trivial regularities in data, and time universal codes can reduce the computation time without losing accuracy.

Abstract #946: Assessment of Acromegaly Patients with and Without Diabetes Treated with Lanreotide Depot: 2-Year Real World Data

2016 ◽  
Vol 22 ◽  
pp. 219
Author(s):  
Roberto Salvatori ◽  
Olga Gambetti ◽  
Whitney Woodmansee ◽  
David Cox ◽  
Beloo Mirakhur ◽  
...  
Keyword(s):  
Real World ◽  
Real World Data ◽  
World Data
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