scholarly journals On the Similarity and Dependence of Time Series

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 550
Author(s):  
Vilém Novák ◽  
Soheyla Mirshahi
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Random Noise ◽  
Similarity Index ◽  
Index Properties ◽  
Fuzzy Transform ◽  
Financial Time ◽  
Selection Of

In this paper, we undertake the problem of evaluating interrelation among time series. Interrelation is measured using a similarity index. In this paper, we suggest a new one based on the known fuzzy transform (F-transform), which has been proven to remove higher frequencies than a given threshold and reduce the random noise significantly. The F-transform also provides an estimation of the slope of time series in a given imprecisely delineated time. We prove some of the suggested index properties and show its ability to measure similarity (and thus the interrelation) on a selection of several real financial time series. The method is well interpretable and easy to adjust.

Model selection of a switching mechanism for financial time series

2016 ◽  
Vol 32 (6) ◽  
pp. 836-851 ◽  
Author(s):  
Buu-Chau Truong ◽  
Cathy W. S. Chen ◽  
Mike K. P. So
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Financial Time Series ◽  
Switching Mechanism ◽  
Financial Time ◽  
Selection Of

Forecasting Financial Time Series using a Hybrid Non-stationary Model with ANN

2019 ◽  
Vol 7 (1) ◽  
pp. 323-326
Author(s):  
D.K.Shetty . ◽  
B.Ismail .
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Stationary Model ◽  
Financial Time

Efficient filtering of financial time series and extreme value theory

2005 ◽  
Vol 7 (2) ◽  
pp. 63-84 ◽  
Author(s):  
Kaj Nyström ◽  
Jimmy Skoglund
Keyword(s):  
Time Series ◽  
Extreme Value Theory ◽  
Financial Time Series ◽  
Value Theory ◽  
Extreme Value ◽  
Financial Time

Two Pitfalls in Comparing Financial Time-Series

2013 ◽  
Author(s):  
Enrico Schumann
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Financial Time

scholarly journals Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Tianle Zhou ◽  
Chaoyi Chu ◽  
Chaobin Xu ◽  
Weihao Liu ◽  
Hao Yu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Phase Space ◽  
Financial Market ◽  
Financial Time Series ◽  
Influential Factors ◽  
Series Data ◽  
Phase Point ◽  
Price Fluctuations ◽  
Financial Time

In this study, a new idea is proposed to analyze the financial market and detect price fluctuations, by integrating the technology of PSR (phase space reconstruction) and SOM (self organizing maps) neural network algorithms. The prediction of price and index in the financial market has always been a challenging and significant subject in time-series studies, and the prediction accuracy or the sensitivity of timely warning price fluctuations plays an important role in improving returns and avoiding risks for investors. However, it is the high volatility and chaotic dynamics of financial time series that constitute the most significantly influential factors affecting the prediction effect. As a solution, the time series is first projected into a phase space by PSR, and the phase tracks are then sliced into several parts. SOM neural network is used to cluster the phase track parts and extract the linear components in each embedded dimension. After that, LSTM (long short-term memory) is used to test the results of clustering. When there are multiple linear components in the m-dimension phase point, the superposition of these linear components still remains the linear property, and they exhibit order and periodicity in phase space, thereby providing a possibility for time series prediction. In this study, the Dow Jones index, Nikkei index, China growth enterprise market index and Chinese gold price are tested to determine the validity of the model. To summarize, the model has proven itself able to mark the unpredictable time series area and evaluate the unpredictable risk by using 1-dimension time series data.

scholarly journals Selecting between causal and noncausal models with quantile autoregressions

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Alain Hecq ◽  
Li Sun
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Latin American ◽  
Financial Time Series ◽  
Selection Criterion ◽  
Likelihood Methods ◽  
Approximate Maximum Likelihood ◽  
Financial Time ◽  
Maximum Likelihood Methods ◽  
Latin American Countries

AbstractWe propose a model selection criterion to detect purely causal from purely noncausal models in the framework of quantile autoregressions (QAR). We also present asymptotics for the i.i.d. case with regularly varying distributed innovations in QAR. This new modelling perspective is appealing for investigating the presence of bubbles in economic and financial time series, and is an alternative to approximate maximum likelihood methods. We illustrate our analysis using hyperinflation episodes of Latin American countries.

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