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1. Kalman Filter Based Electricity Market States Forecasting: A State-Space Framework

   Rana, Md Masud; Bo, Rui; Choi, Bong Jun (August 2019, Proceedings of 9th IEEE International Conference on CYBER Technology in Automation, Control and Intelligent Systems (IEEE-CYBER 2019))

   Gaining money and high profit is the dream of electricity market investors; however, it requires accurate financial knowledge and price forecasting ability. Most of the investors are used the electricity market historical information for forecasting power generation, consumption, and utility price. Unfortunately, electricity market time-series profile is high volatility and change over time, so the factual data cannot accurately reflect the electricity market states such as power consumption and generation. In the literature, there is no systematic way or suitable models that can fit, analyze, and predict electricity market system states over time. Interestingly, this paper proposes an electricity market state-space model which is obtained by a set of electricity market differential equations. After simplifying of these equations, the continuous-time electricity market state-space model is derived. Using discrete-time step size parameter, the continuous-time system is discretised. Furthermore, the noisy measurements are obtained by a set of smart sensors. Finally, the Kalmna filter based electricity market state forecasting algorithm is developed based on noisy measurements. Simulation results show that the proposed algorithm can properly forecast the electricity market states. Consequently, this kind of model and algorithm can help to develop the electricity market simulator and assist investor to participate/invest electricity market regardless of the world economic downtown. 

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2. A Chlorophyll Biomass Time-Series for the Distributed Biological Observatory in the Context of Seasonal Sea Ice Declines in the Pacific Arctic Region

   https://doi.org/10.3390/geosciences12080307  

   Cooper, Lee W.; Grebmeier, Jacqueline M. (August 2022, Geosciences)

   Declines in seasonal sea ice in polar regions have stimulated projections of how primary production has shifted in response to greater light penetration over a longer open water season. Despite the limitations of remotely sensed observations in an often cloudy environment, remote sensing data provide strong indications that surface chlorophyll biomass has increased (since 2000) as sea ice has declined in the Pacific Arctic region. We present here shipboard measurements of chlorophyll-a that have been made annually in July since 2000 from the Distributed Biological Observatory (DBO) stations in the Bering Strait region. This time series as well as shipboard observations made in other months since the late 1980s implicate complexities that intrude on a simple expectation that, as open water periods increase, the production and biomass of phytoplankton will increase predictably. These shipboard observations indicate that there have not been sharp increases in chlorophyll-a, for either maxima observed in the water column or integrated over the whole water column, at the DBO stations over a time-series extending for as long as 20 years coinciding with seasonal sea ice declines. On the other hand, biomass may be increasing in other months: we provide a shipboard confirmation of a fall bloom in October as wind mixing introduced nutrients back into the upper water column. The productive DBO stations may be at a high enough production already that additional enhancements in chlorophyll-a biomass should not be expected, but our time-series record does not exclude the possibility that additional enhanced production may be present in other areas outside the DBO station grid. These findings may also reflect limitations imposed by nutrient cycling and water column structure. The increasing freshwater component of waters flowing through the Bering Strait is likely associated with increased stratification that limits the potential change in biological production associated with decreases in seasonal sea ice persistence. 

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3. Endogenous Inverse Demand Functions

   https://doi.org/10.1287/opre.2022.2325  

   Bichuch, Maxim; Feinstein, Zachary (January 2022, Operations Research)

   In this work we present an equilibrium formulation for price impacts. This is motivated by the Bühlmann equilibrium in which assets are sold into a system of market participants, for example, a fire sale in systemic risk, and can be viewed as a generalization of the Esscher premium. Existence and uniqueness of clearing prices for the liquidation of a portfolio are studied. We also investigate other desired portfolio properties including monotonicity and concavity. Price per portfolio unit sold is also calculated. In special cases, we study price impacts generated by market participants who follow the exponential utility and power utility. 

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4. Fifteen Years of (Major to Great) Tsunamigenic Earthquakes

   https://doi.org/10.1016/B978-0-12-409548-9.11767-1  

   Romano, F.; Lorito, S.; Piatanesi, A.; Lay, T. (April 2020, Earth systems and environment)

   Tsunamis are a series of long gravity waves generated by abrupt displacement of a significant volume of water that propagate under the action of gravity, returning the water layer to an equilibrium position. Differently from common wind-generated waves, tsunamis are characterized by large wavelengths (ranging from tens to hundreds of km) and long periods (ranging from minutes to hours) with water motions extending across the entire depth range of the water layer. Several natural phenomena such as earthquakes, landslides, volcanic eruptions, rapid changes of atmospheric pressure (meteotsunami), or asteroids impacts can be the source of the initial water displacement of a tsunami; among these, the most frequent involves submarine earthquakes. Most tsunamigenic earthquakes occur near Earth’s convergent plate tectonic boundaries (Fig. 1), at subduction zones, where an oceanic plate underthrusts another plate. During the last 15 years (2004-2019), subduction zones hosted a particularly high number of great magnitude tsunamigenic earthquakes with catastrophic consequences in terms of casualties and damages. This spate of activity is consistent with normal statistical fluctuations of seismicity rate over the past century (Lay, 2015), however, the collective tsunami impact was particularly severe in terms of fatalities, people missing, building destruction, and damage to infrastructures (e.g., ports, power plants, coastal towns). The largest tsunamis since 2004 were those ensuing from the 2004 Mw 9.2 Sumatra-Andaman (Indonesia) and the 2011 Mw 9.0 Tohoku (Japan) earthquakes (e.g., Lorito et al., 2016), causing 220,000+ and 15,000+ victims, respectively. 

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5. Why topological data analysis detects financial bubbles?

   https://doi.org/10.1016/j.cnsns.2023.107665  

   Akingbade, Samuel W; Gidea, Marian; Manzi, Matteo; Nateghi, Vahid (January 2024, Communications in Nonlinear Science and Numerical Simulation)

   We present a heuristic argument for the propensity of Topological Data Analysis (TDA) to detect early warning signals of critical transitions in financial time series. Our argument is based on the Log-Periodic Power Law Singularity (LPPLS) model, which characterizes financial bubbles as super-exponential growth (or decay) of an asset price superimposed with oscillations increasing in frequency and decreasing in amplitude when approaching a critical transition (tipping point). We show that whenever the LPPLS model is fitting with the data, TDA generates early warning signals. As an application, we illustrate this approach on a sample of positive and negative bubbles in the Bitcoin historical price. 

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