Prediction of daily COVID-19 cases in European countries using automatic ARIMA model

The recent pandemic (COVID-19) emerged in Wuhan city of China and after causing a lot of destruction there recently changed its epicenter to Europe. There are countless people affected and reported cases are increasing day by day. Predictive models need to consider previous reported cases and forecast the upcoming number of cases. Automatic ARIMA, one of the predictive models used for forecasting contagions, was used in this study to predict the number of confirmed cases for next 10 days in four top European countries through R package “forecast”. The study finds that Auto ARIMA applied on the sample satisfactorily forecasts the confirmed cases of coronavirus for next ten days. The confirmed cases for the four countries show an increasing trend for the next ten days with Spain with a highest number of expected new confirmed cases, followed by Germany and France. Italy is expected to have lowest number of new confirmed cases among the four countries

Modeling Dynamic Multifractal Efficiency of US Electricity Market

N/

Intraday Volatility Spillovers among European Financial Markets during COVID-19

N/

A complex networks based analysis of jump risk in equity returns:An evidence using intraday movements from Pakistan stock market

International audienceWe employ a multi-stage methodology combining complex network analytics and financial risk modelling to unveil the correlation structures amongst the price jump risks of companies forming the KSE-100 index in Pakistan. We identify the most influential companies in terms of jump risk, and identify communities — clusters of companies with similar price movement characteristics or with highly correlated price jumps. We find that equities in Pakistan stock market experience jumps in different time periods that are correlated to varying degrees within and across industries resulting in 19 different communities, four of which are strongly connected. While Oil & Gas, Cement and Banking sectors exhibit a significant representation of firms in communities, the automobile industry, however, seems to play an important role in risk propagation. These results provide an interesting insight to investors and other stakeholders from an emerging market viewpoint identifying the major sectors driving the volatility of KSE-100 index

Is Brazilian music getting more predictable? A statistical physics approach for different music genres

Music is an important part of most people's lives and also of the culture of a country. Moreover, the different characteristics of songs, such as genre and the chord sequences, could have different impacts on individual behaviours. Even considering just seven chords and the respective variations, originality can be a crucial element of a song's success. Considering this, and in the context of Brazilian music, we employed the Detrended Fluctuation Analysis to analyse the possible predictability of eight different music genres. On these genres, we found that Reggae and Pop seem to be the least random considering the sequenced use of chords. With a sliding windows approach, we found that the predictability of chord sequences of Pop decreased over time. Applying the same methodology after shuffling the original series of music, the results point to a randomness of those shuffled series, demonstrating the robustness of our approach

Enhanced Energy Savings with Adaptive Watchful Sleep Mode for Next Generation Passive Optical Network

A single watchful sleep mode (WSM) combines the features of both cyclic sleep mode (CSM) and cyclic doze mode (CDM) in a single process by periodically turning ON and OFF the optical receiver (RX) of the optical network terminal (ONT) in a symmetric manner. This results in almost the same energy savings for the ONTs as achieved by the CSM process while significantly reducing the upstream delays. However, in this study we argue that the periodic ON and OFF periods of the ONT RX is not an energy efficient approach, as it reduces the ONT Asleep (AS) state time. Instead, this study proposes an adaptive watchful sleep mode (AWSM) in which the RX ON time of ONT is minimized during ONT Watch state by choosing it according to the length of the traffic queue of the type 1 (T1) traffic class. The performance of AWSM is compared with standard WSM and CSM schemes. The investigation reveals that by minimizing the RX ON time, the AWSM scheme achieves up to 71% average energy saving per ONT at low traffic loads. The comparative study results show that the ONT energy savings achieved by AWSM are 9% higher than the symmetric WSM with almost the same delay and delay variance performance

FFRP: Dynamic firefly mating optimization inspired energy efficient routing protocol for internet of underwater wireless sensor networks

Energy-efficient and reliable data gathering using highly stable links in underwater wireless sensor networks (UWSNs) is challenging because of time and location-dependent communication characteristics of the acoustic channel. In this paper, we propose a novel dynamic firefly mating optimization inspired routing scheme called FFRP for the internet of UWSNs-based events monitoring applications. The proposed FFRP scheme during the events data gathering employs a self-learning based dynamic firefly mating optimization intelligence to find the highly stable and reliable routing paths to route packets around connectivity voids and shadow zones in UWSNs. The proposed scheme during conveying information minimizes the high energy consumption and latency issues by balancing the data traffic load evenly in a large-scale network. In additions, the data transmission over highly stable links between acoustic nodes increases the overall packets delivery ratio and network throughput in UWSNs. Several simulation experiments are carried out to verify the effectiveness of the proposed scheme against the existing schemes through NS2 and AquaSim 2.0 in UWSNs. The experimental outcomes show the better performance of the developed protocol in terms of high packets delivery ratio (PDR) and network throughput (NT) with low latency and energy consumption (EC) compared to existing routing protocols in UWSNs

On the Efficiency of Foreign Exchange Markets in times of the COVID-19 Pandemic

We employ multifractal detrended fluctuation analysis (MF-DFA) to provide the first look at the efficiency of forex markets during the initial period of ongoing COVID-19 pandemic, which has disrupted the financial markets globally. We use high frequency (5-min interval) data of six major currencies traded in the forex market for the period from 01 October 2019 to 31 March 2020. Prior to the application of MF-DFA, we examine the inner dynamics of multifractality using seasonal-trend decompositions using loess (STL) method. Overall, the results confirm the presence of multifractality in forex markets, which demonstrates, in particular: (i) a decline in the efficiency of forex markets during the period of COVID-19 outbreak, and (ii) the heterogeneity in the effects on the strength of multifractality of exchange rate returns under investigation. The largest effect is observed in the case of AUD as it shows the highest (lowest) efficiency before (during) COVID-19 assessed in terms of low (high) multifractality. During COVID-19 period, CAD and CHF exhibit the highest efficiency. Our findings may help policymakers in shaping a comprehensive response to improve the forex market efficiency during such a black swan event