scholarly journals Влияние шума наблюдений на эффект ложной связи между осцилляторами при оценивании по временным рядам

2019 ◽  
Vol 45 (16) ◽  
pp. 6
Author(s):  
С.Н. Крылов ◽  
Д.А. Смирнов ◽  
Б.П. Безручко
Keyword(s):  
Time Series ◽  
Quantitative Analysis ◽  
Complex Systems ◽  
Granger Causality ◽  
Mutual Coupling ◽  
Noise Levels ◽  
Observational Noise

The practice of identifying structure of couplings between elements of complex systems from experimental recordings of their oscillations (time series) using the Wiener – Granger causality method has revealed a number of problems that prevent one from obtaining reliable results. In particular, the presence of observational noise can lead to the “effect of spurious coupling”, i.e. to inference of mutual coupling between two elements that are actually coupled in a unidirectional way. A quantitative analysis of this phenomenon is carried out and recommendations allowing one to reduce its probability are presented. It is shown that the effect typically takes place only for large noise levels comparable to the level of observed oscillations. However, we have also singled out less typical situations where the effect occurs at much weaker noise.

scholarly journals Observability of Complex Systems by Means of Relative Distances Between Homological Groups

2020 ◽  
Vol 8 ◽  
Author(s):  
Juan G. Diaz Ochoa
Keyword(s):  
Time Series ◽  
Data Structure ◽  
Quantitative Analysis ◽  
Complex Systems ◽  
Large Datasets ◽  
Mobile Sensors ◽  
Good Strategy ◽  
Data Intensive ◽  
Persistent Topology ◽  
Definition Of

It is common to consider using a data-intensive strategy as a way to develop systemic and quantitative analysis of complex systems so that data collection, sampling, standardization, visualization, and interpretation can determine how causal relationships are identified and incorporated into mathematical models. Collecting enough large datasets seems to be a good strategy in reducing bias of the collected data; but persistent and dynamic anomalies in the data structure, generated from variations in intrinsic mechanisms, can actually induce persistent entropy thus affecting the overall validity of quantitative models. In this research, we are introducing a method based on the definition of homological groups that aims at evaluating this persistent entropy as a complexity measure to estimate the observability of the systems. This method identifies patterns with persistent topology, extracted from the combination of different time series and clustering them to identify persistent bias in the data. We tested this method on accumulated data from patients using mobile sensors to measure the response of physical exercise in real-world conditions outside the lab. With this method, we aim to better stratify time series and customize models in complex biological systems.

scholarly journals Evaluating the performance of multivariate indicators of resilience loss

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Els Weinans ◽  
Rick Quax ◽  
Egbert H. van Nes ◽  
Ingrid A. van de Leemput
Keyword(s):  
Time Series ◽  
Complex Systems ◽  
Multivariate Time Series ◽  
Tipping Points ◽  
Physical And Mental Health ◽  
One Dimensional ◽  
Slowing Down ◽  
The Past ◽  
Technological Developments ◽  
Resilience Indicators

AbstractVarious complex systems, such as the climate, ecosystems, and physical and mental health can show large shifts in response to small changes in their environment. These ‘tipping points’ are notoriously hard to predict based on trends. However, in the past 20 years several indicators pointing to a loss of resilience have been developed. These indicators use fluctuations in time series to detect critical slowing down preceding a tipping point. Most of the existing indicators are based on models of one-dimensional systems. However, complex systems generally consist of multiple interacting entities. Moreover, because of technological developments and wearables, multivariate time series are becoming increasingly available in different fields of science. In order to apply the framework of resilience indicators to multivariate time series, various extensions have been proposed. Not all multivariate indicators have been tested for the same types of systems and therefore a systematic comparison between the methods is lacking. Here, we evaluate the performance of the different multivariate indicators of resilience loss in different scenarios. We show that there is not one method outperforming the others. Instead, which method is best to use depends on the type of scenario the system is subject to. We propose a set of guidelines to help future users choose which multivariate indicator of resilience is best to use for their particular system.

scholarly journals Modeling Noisy Time Series: Physiological Tremor

1998 ◽  
Vol 08 (07) ◽  
pp. 1505-1516 ◽  
Author(s):  
J. Timmer
Keyword(s):  
Time Series ◽  
Stochastic Systems ◽  
State Space Model ◽  
Physiological Tremor ◽  
Space Model ◽  
Deterministic Systems ◽  
Estimated Parameters ◽  
Noisy Time Series ◽  
Observational Noise ◽  
Small Amplitude Oscillation

Empirical time series often contain observational noise. We investigate the effect of this noise on the estimated parameters of models fitted to the data. For data of physiological tremor, i.e. a small amplitude oscillation of the outstretched hand of healthy subjects, we compare the results for a linear model that explicitly includes additional observational noise to one that ignores this noise. We discuss problems and possible solutions for nonlinear deterministic as well as nonlinear stochastic processes. Especially we discuss the state space model applicable for modeling noisy stochastic systems and Bock's algorithm capable for modeling noisy deterministic systems.

scholarly journals Risks and the influence of negative interest rates on economic activity: a case study of Sweden, Denmark, and Switzerland

2020 ◽  
Vol 15 (1) ◽  
pp. 30-41
Author(s):  
Liběna Černohorská ◽  
Darina Kubicová
Keyword(s):  
Time Series ◽  
Interest Rates ◽  
Granger Causality ◽  
Economic Activity ◽  
Central Banks ◽  
Time Interval ◽  
Short Term ◽  
Stock Indices ◽  
Negative Interest Rates ◽  
The Impact

The purpose of this paper is to analyze the impact of negative interest rates on economic activity in a selected group of countries, in particular Sweden, Denmark, and Switzerland, for the period 2009–2018. The central banks of these countries were among the first to implement negative interest rates to revive the economic growth. Therefore, this study analyzed long- and short-term relationships between interest rates announced by central banks and gross domestic product and blue chip stock indices. Time series analysis was conducted using Engle-Granger cointegration analysis and Granger causality testing to identify long- and short-term relationship. The first step, using the Akaike criteria, was to determine the optimal delay of the entire time interval for the analyzed periods. Time series that seem to be stationary were excluded based on the results of the Dickey-Fuller test. Further testing continued with the Engle-Granger test if the conditions were met. It was designed to identify co-integration relationships that would show correlation between the selected variables. These tests showed that at a significance level of 0.05, there is no co-integration between any time series in the countries analyzed. On the basis of these analyses, it was determined that there were no long-term relationships between interest rates and GDP or stock indices for these countries during the monitored time period. Using Granger causality, the study only confirmed short-term relationship between interest rates and GDP for all examined countries, though not between interest rates and the stock indices. Acknowledgment The paper has been created with the financial support of The Czech Science Foundation GACR 18-05244S – Innovative Approaches to Credit Risk Management.

scholarly journals Time Series Adaptive Online Prediction Method Combined with Modified LS-SVR and AGO

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Guo Yangming ◽  
Zhang Lu ◽  
Cai Xiaobin ◽  
Ran Congbao ◽  
Zhai Zhengjun ◽  
...  
Keyword(s):  
Time Series ◽  
Complex Systems ◽  
Incremental Learning ◽  
Learning Algorithm ◽  
Computing Time ◽  
Grey System Theory ◽  
Good Prediction ◽  
Grey System ◽  
Online Prediction ◽  
Prediction Approach

Fault or health condition prediction of the complex systems has attracted more attention in recent years. The complex systems often show complex dynamic behavior and uncertainty, which makes it difficult to establish a precise physical model. Therefore, the time series of complex system is used to implement prediction in practice. Aiming at time series online prediction, we propose a new method to improve the prediction accuracy in this paper, which is based on the grey system theory and incremental learning algorithm. In this method, the accumulated generating operation (AGO) with the raw time series is taken to improve the data quality and regularity firstly; then the prediction is conducted by a modified LS-SVR model, which simplifies the calculation process with incremental learning; finally, the inverse accumulated generating operation (IAGO) is performed to get the prediction results. The results of the prediction experiments indicate preliminarily that the proposed scheme is an effective prediction approach for its good prediction precision and less computing time. The method will be useful in actual application.

Analyzing Granger Causality in Climate Data with Time Series Classification Methods

2017 ◽  
pp. 15-26 ◽  
Author(s):  
Christina Papagiannopoulou ◽  
Stijn Decubber ◽  
Diego G. Miralles ◽  
Matthias Demuzere ◽  
Niko E. C. Verhoest ◽  
...  
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Time Series Classification ◽  
Classification Methods ◽  
Climate Data

HUBUNGAN KAUSALITAS HARGA JUAL DAN HARGA SEWA APARTEMEN DI JABODETABEK

2021 ◽  
Vol 1 (1) ◽  
pp. 93-105
Author(s):  
Zainal Zawir Simon ◽  
Effendy Zain ◽  
Zulihar Zulihar
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Time Series Data ◽  
Investment Analysis ◽  
Series Data ◽  
Analysis Tool ◽  
Selling Price ◽  
Causality Analysis ◽  
Granger Causality Analysis ◽  
Causality Relationship

Abstrak Penelitian ini bertujuan untuk mengetahui hubungan kausalitas antara harga jual apartemen dan harga sewa apartemen di wilayah Jabodetabek. Data yang dipergunakan adalah data  time series dalam bentuk kuartalan untuk periode 2007:1-2018:3 dan alat analisis yang dipergunakan adalah analisa kausalitas Granger. Hasil penelitian menunjukkan bahwa tidak terdapat hubungan kausalitas antara harga jual apartemen dan harga sewa apartemen di wilayah Jabodetabek. Dengan kata lain perubahan harga jual  tidak mempengaruhi harga sewa. Sebaliknya harga sewa juga tidak mempengaruhi harga jual apartemen. Dengan demikian Investor diharapkan dalam melakukan analisis investasinya memasukkan faktor-faktor lain yang dapat mempengaruhi harga jual dan harga sewa untuk apartemen, agar terlepas dari pandangan bahwa harga jual mempengaruhi harga sewa dan sebaliknya.Kata Kunci : Harga Jual apartemen, Harga Sewa Apartemen, Data Runtut Waktu, Analisa Kausalitas GrangerABSTRACTThis study aims to determine the causality relationship between the selling price of apartments and apartment rental prices in the Greater Jakarta area. The data used are time series data in quarterly form for the period 2007: 1-2018: 3 and the analysis tool used is the Granger causality analysis. The results showed that there was no causality relationship between apartment selling prices and apartment rental prices in the Greater Jakarta area. In other words, changes in selling prices do not affect rental prices. Conversely the rental price also does not affect the selling price of the apartment. Thus Investors are expected to carry out investment analysis to include other factors that can affect the selling price and rental price for an apartment, so that regardless of the view that the selling price affects the rental price and vice versa.Keywords : Selling Price of apartments, rental prices apartments, time series data, Granger Causality Analysis

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