A Chaos Analysis of the Dry Bulk Shipping Market

Finding low-dimensional chaos is a relevant issue as it could allow short-term reliable forecasting. However, the existence of chaos in shipping freight rates remains an open and outstanding matter as previous research used methodology that can produce misleading results. Using daily data, this paper aims to unveil the nonlinear dynamics of the Baltic Dry Index that has been proposed as a measure of the shipping rates for certain raw materials. We tested for the existence of nonlinearity and low-dimensional chaos. We have also examined the chaotic dynamics throughout three sub-sampling periods, which have been determined by the volatility pattern of the series. For this purpose, from a comprehensive view we apply several metric and topological techniques, including the most suitable methods for noisy time series analysis. The proposed methodology considers the characteristics of chaotic time series, such as nonlinearity, determinism, sensitivity to initial conditions, fractal dimension and recurrence. Although there is strong evidence of a nonlinear structure, a chaotic and, therefore, deterministic behavior cannot be assumed during the whole or the three periods considered. Our findings indicate that the generalized autoregressive conditional heteroscedastic (GARCH) model and exponential GARCH (EGARCH) model explain a significant part of the nonlinear structure that is found in the dry bulk shipping freight market.

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CHAOTIC ANALYSIS OF A TIME SERIES COMPOSED OF SEISMIC EVENTS RECORDED IN JAPAN

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127494000071 ◽

1994 ◽

Vol 04 (01) ◽

pp. 87-98 ◽

Cited By ~ 14

Author(s):

G.P. PAVLOS ◽

L. KARAKATSANIS ◽

J.B. LATOUSSAKIS ◽

D. DIALETIS ◽

G. PAPAIOANNOU

Keyword(s):

Time Series ◽

Lyapunov Exponent ◽

Initial Conditions ◽

Low Frequency ◽

Underlying Mechanism ◽

Largest Lyapunov Exponent ◽

Seismic Events ◽

Earthquake Dynamics ◽

The Largest Lyapunov Exponent ◽

Low Dimensional

A chaotic analysis approach was applied to an earthquake time series recorded in the Japanese area in order to test the assumption that the earthquake process could be the manifestation of a chaotic low dimensional process. For the study of the seismicity we have used a time series consisting of time differences between two consecutive seismic events with magnitudes greater than 2.6. The results of our study show that the underlying mechanism, as expressed by the time series, can be described by low dimensional chaotic dynamics. The power spectrum of the time series shows a power law profile with two slopes, α=1.4 in the low frequency and α=0.05 in the high frequency regions, while the slopes of the correlation integrals show an apparent plateau at the scaling region, which saturates at the value D≈3.2. The largest Lyapunov exponent was found to be ≈0.9. The positive value of the largest Lyapunov exponent reveals strong sensitivity to initial conditions of the supposed earthquake dynamics.

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The Relationship Between Commodity Prices and Freight Rates in the Dry Bulk Shipping Segment: A Threshold Regression Approach

SSRN Electronic Journal ◽

10.2139/ssrn.3581592 ◽

2020 ◽

Author(s):

Konstantinos Melas ◽

Nektarios Michail

Keyword(s):

Commodity Prices ◽

Threshold Regression ◽

Freight Rates ◽

Bulk Shipping ◽

Regression Approach ◽

The Relationship

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Time Series Forecasting methods for the Baltic Dry Index (BDI)

Journal of Forecasting ◽

10.1002/for.2780 ◽

2021 ◽

Author(s):

Christos Katris ◽

Manolis G. Kavussanos

Keyword(s):

Time Series ◽

Time Series Forecasting ◽

Forecasting Methods ◽

The Baltic ◽

Baltic Dry Index

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Profitability of the Northern Sea Route for liquid bulk shipping under post 2020 sulphur regulations

The International Journal of Logistics Management ◽

10.1108/ijlm-12-2018-0314 ◽

2020 ◽

Vol 31 (2) ◽

pp. 313-332

Author(s):

Tuomo Keltto ◽

Su-Han Woo

Keyword(s):

Spot Market ◽

The Arctic ◽

Future Research ◽

Estimation Model ◽

Content Type ◽

Freight Rates ◽

Northern Sea Route ◽

Financial Perspective ◽

The Baltic ◽

Regulation Compliance

PurposeThe purpose of this study is to evaluate the profitability of the Northern Sea Route (NSR) as a shipping lane from the financial perspective of shipping companies under post 2020 sulphur regulations.Design/methodology/approachThis study develops profit estimation model, and the profitability of the NSR is assessed for a Handymax Medium Range (MR) tanker vessel using scenarios in combination with spot market earning levels, the regulation compliance method and destination ports. The required freight rates are calculated to justify the decision of shipowners to transit a tanker from the Baltic spot market to the NSR navigation.FindingsResults suggest that the required freight rates from the Arctic trade to justify the transit to the NSR are higher than the actual agreed rates in the past, which implies low viability of the NSR as a regular shipping lane. It was also found that the required freight rates are affected by the spot market earning levels, compliance method and duration of the voyage.Research limitations/implicationsThis study takes a new approach on assessing the NSR viability by comprehensively assessing the annual profitability and including the spot market trade as an opportunity cost for the NSR shipping. Despite various scenarios used in this study, a sensitivity analysis would be useful for future research.Practical implicationsThis study suggests how much freight rates a shipping company would need to charge if it were to offer tanker shipping services to four major Asian ports while simultaneously operating at the Baltic Sea during the remainder of the year.Originality/valueThis study adopts a market-oriented approach by incorporating both earnings and costs (including opportunity costs) in the profitability model rather than merely analyzing the total cost of shipping via the NSR. This study also analyzes impact of IMO 2020 Sulphur regulation on the NSR profitability.

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LEARNING DYNAMICS AND NONLINEAR MISSPECIFICATION IN AN ARTIFICIAL FINANCIAL MARKET

Macroeconomic Dynamics ◽

10.1017/s1365100509080262 ◽

2009 ◽

Vol 13 (5) ◽

pp. 625-655 ◽

Cited By ~ 3

Author(s):

Christophre Georges ◽

John C. Wallace

Keyword(s):

Time Series ◽

Financial Market ◽

Consistency Condition ◽

Nonlinear Structure ◽

State Variable ◽

Bounded Memory ◽

Long Run ◽

Finite Memory ◽

Aggregate Dynamics ◽

True Structure

In this paper, we explore the consequence of learning to forecast in a very simple environment. Agents have bounded memory and incorrectly believe that there is nonlinear structure underlying the aggregate time series dynamics. Under social learning with finite memory, agents may be unable to learn the true structure of the economy and rather may chase spurious trends, destabilizing the actual aggregate dynamics. We explore the degree to which agents' forecasts are drawn toward a minimal state variable learning equilibrium as well as a weaker long-run consistency condition.

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Yet another assessment of climate change in the Baltic Sea area: Breakpoints in climate time series

2010 IEEE/OES Baltic International Symposium (BALTIC) ◽

10.1109/baltic.2010.5621643 ◽

2010 ◽

Cited By ~ 1

Author(s):

A. K. Stips ◽

M. Lilover

Keyword(s):

Climate Change ◽

Time Series ◽

Baltic Sea ◽

The Baltic Sea ◽

Climate Time Series ◽

The Baltic ◽

Sea Area

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Deep Learning Based Classification of Time Series of Chaotic Systems over Graphic Images

10.21203/rs.3.rs-1138927/v1 ◽

2021 ◽

Author(s):

Süleyman UZUN ◽

Sezgin KAÇAR ◽

Burak ARICIOĞLU

Keyword(s):

Time Series ◽

Deep Learning ◽

Transfer Learning ◽

Chaotic Systems ◽

Initial Conditions ◽

Step Size ◽

Learning Methods ◽

Data Set ◽

Graphic Images ◽

Different Chaotic Systems

Abstract In this study, for the first time in the literature, identification of different chaotic systems by classifying graphic images of their time series with deep learning methods is aimed. For this purpose, a data set is generated that consists of the graphic images of time series of the most known three chaotic systems: Lorenz, Chen, and Rossler systems. The time series are obtained for different parameter values, initial conditions, step size and time lengths. After generating the data set, a high-accuracy classification is performed by using transfer learning method. In the study, the most accepted deep learning models of the transfer learning methods are employed. These models are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet101, DenseNet201, ShuffleNet and GoogLeNet. As a result of the study, classification accuracy is found between 96% and 97% depending on the problem. Thus, this study makes association of real time random signals with a mathematical system possible.

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LSTM-Guided Coaching Assistant for Table Tennis Practice

Sensors ◽

10.3390/s18124112 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4112 ◽

Cited By ~ 6

Author(s):

Se-Min Lim ◽

Hyeong-Cheol Oh ◽

Jaein Kim ◽

Juwon Lee ◽

Jooyoung Park

Keyword(s):

Time Series ◽

State Space ◽

Time Series Data ◽

State Space Model ◽

Skill Assessment ◽

Series Data ◽

High Dimensional ◽

Table Tennis ◽

Space Model ◽

Low Dimensional

Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching.

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