Symbolic time series analysis and dynamic regimes

Brida, Juan Gabriel; Punzo, Lionello F.

doi:10.1016/s0954-349x(02)00050-4

Cited by 48 publications

(28 citation statements)

References 20 publications

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“…The logistic Map is a 1-dimensional chaotic dynamic system which is commonly employed as benchmark to study tools and methods used to characterize chaotic dynamics [ 19 , 20 , 21 ]. The logistic map function is defined as

where

is a random number within

, and r is a constant.…”

Section: Methodsmentioning

confidence: 99%

Time Series Complexities and Their Relationship to Forecasting Performance

Ponce-Flores

Frausto–Solís

Santamaría-Bonfil

et al. 2020

Entropy

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Entropy is a key concept in the characterization of uncertainty for any given signal, and its extensions such as Spectral Entropy and Permutation Entropy. They have been used to measure the complexity of time series. However, these measures are subject to the discretization employed to study the states of the system, and identifying the relationship between complexity measures and the expected performance of the four selected forecasting methods that participate in the M4 Competition. This relationship allows the decision, in advance, of which algorithm is adequate. Therefore, in this paper, we found the relationships between entropy-based complexity framework and the forecasting error of four selected methods (Smyl, Theta, ARIMA, and ETS). Moreover, we present a framework extension based on the Emergence, Self-Organization, and Complexity paradigm. The experimentation with both synthetic and M4 Competition time series show that the feature space induced by complexities, visually constrains the forecasting method performance to specific regions; where the logarithm of its metric error is poorer, the Complexity based on the emergence and self-organization is maximal.

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where

is a random number within

, and r is a constant.…”

Section: Methodsmentioning

confidence: 99%

Time Series Complexities and Their Relationship to Forecasting Performance

Ponce-Flores

Frausto–Solís

Santamaría-Bonfil

et al. 2020

Entropy

View full text Add to dashboard Cite

show abstract

“…, s T } such that s t = j if and only if ( y t , g yt ) belongs to R j . This symbolic sequence summarises all the information about regime dynamics of the country (see Brida, Puchet Anyul, and Punzo 2003 and Brida and Punzo 2003 for a detailed exposition about regime dynamics and its symbolic representation). To compare and classify the 140 countries according to their economic performances, we divide them into different clusters obtained through a non‐parametric methodology based on the construction of a minimal spanning tree (MST) and a HT.…”

Section: Methodsmentioning

confidence: 99%

An Alternative View of the Convergence Issue of Growth Empirics

et al. 2011

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In this paper, we study the dynamics of economic growth for 140 countries during the period 1951-2003. The variables representing economic performance are levels and growth rates of per capita gross domestic product. Using the concept of economic regime, we introduce a notion of distance between the dynamical paths of different countries. Then, a minimal spanning tree and a hierarchical tree are constructed from time series to help detect the existence of groups of countries sharing similar economic performance. The two main clusters that are identified over the whole-time interval can be interpreted as two groups of countries with high and low performance, respectively. The evolution of such clusters shows three main stylised facts: Certain countries move across clusters; the high-performance cluster tends to span, while the lowperformance one tends to be (more) compact; and the distance between the two groups increases in time.

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“…Symbolization permits to transform the two-dimensional problem in one dimensional and then to apply the symbolic test as explained. We can follow a similar approach as in [66] where STSA is applied Time Series Analysis and Applicationsto dynamic regimes. Figure 2 shows the transformation of the variable (G, P) in a symbolic variable with an alphabet of four symbols (I: low economic growth and low inflation, II: low economic growth and high inflation, III: high economic growth and high inflation, IV: high economic growth and low inflation) considering as partition the mean of each variable.…”

Section: Sample Sizementioning

confidence: 99%

Symbolic Time Series Analysis and Its Application in Social Sciences

Risso¹

2018

Time Series Analysis and Applications

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The present chapter intents to present the symbolic time series analysis (STSA) reviewing the recent developments in sciences. Even if there are very few works applied to social sciences, STSA has a potential to be developed. In particular, due to the limitations about historical data, fields such as Economics and Finance need to develop statistical tests to prove their hypotheses. An independence test and a causality test based on STSA are reviewed. They seem to be more powerful, detecting different kinds of nonlinearities compared with the classical tests, usually applied in social sciences. However, there is much work to do with STSA, and social sciences are a fertile field for the development of new powerful tools.

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Symbolic time series analysis and dynamic regimes

Cited by 48 publications

References 20 publications

Time Series Complexities and Their Relationship to Forecasting Performance

Time Series Complexities and Their Relationship to Forecasting Performance

An Alternative View of the Convergence Issue of Growth Empirics

Symbolic Time Series Analysis and Its Application in Social Sciences

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