A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Machine learning improves subseasonal forecasts for Western U.S. climate.
New method models precipitation extremes and spatial dependence.
In this paper we have analyzed scaling properties of time series of stock market indices (SMIs) of developing economies of Western Balkans, and have compared the results we have obtained with the results from more developed economies. We have used three different techniques of data analysis to obtain and verify our fin…
I examine global recessions as a cascade phenomenon. In other words, how recessions arising in one or more countries might percolate across a network of connected economies. A heterogeneous agent based model is set up in which the agents are Western economies. A country has a probability of entering a recession in any …
We investigate the average frequency of positive slope , crossing for the returns of market prices. The method is based on stochastic processes which no scaling feature is explicitly required. Using this method we define new quantity to quantify stage of development and activity of stocks exchange. We compare …
Using open source data, we observe the fascinating dynamics of nighttime light. Following a global economic regime shift, the planetary center of light can be seen moving eastwards at a pace of about 60 km per year. Introducing spatial light Gini coefficients, we find a universal pattern of human settlements across dif…
Paper proposes combining GAM and DNN for accurate peak demand estimation from lower-resolution data.
Econophysics provides a strategy for understanding the potential mechanisms underlying the anomalous distribution of wealth found in real societies. We present a computational nonlinear stochastic model for the distribution of wealth that depends upon three parameters and two mechanisms: trade and investment. To avoid …
Almost universally, wealth is not distributed uniformly within societies or economies. Even though wealth data have been collected in various forms for centuries, the origins for the observed wealth-disparity and social inequality are not yet fully understood. Especially the impact and connections of human behavior on …
Trains neural machine translation models for 5 Southern African languages.
The higher-end tail of the wealth distribution in India is studied using recently published lists of the wealth of richest Indians between the years 2002-4. The resulting rank distribution seems to imply a power-law tail for the wealth distribution, with a Pareto exponent between 0.81 and 0.92 (depending on the year un…
Study finds stock search trends correlate with developing economies' stock indices.
Since August 2000, the stock market in the USA as well as most other western markets have depreciated almost in synchrony according to complex patterns of drops and local rebounds. In \cite{SZ02QF}, we have proposed to describe this phenomenon using the concept of a log-periodic power law (LPPL) antibubble, characteriz…
Improved music source separation using spectrogram feature loss.
We consider the problem of modeling discrete-valued vector time series data using extensions of Chow-Liu tree models to capture both dependencies across time and dependencies across variables. Conditional Chow-Liu tree models are introduced, as an extension to standard Chow-Liu trees, for modeling conditional rather th…
Study finds Binance's tether-margined contracts significantly impact bitcoin volatility.
This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamic filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependen…
In 1969 M. Gromov in his PhD thesis greatly generalized Smale-Hirsch-Phillips immersion-submersion theory by proving what is now called the h-principle for invariant open differential relations over open manifolds. Gromov extracted the original geometric idea of Smale and put it to work in the maximal possible generali…
A GAN-based method diagnoses faults in imbalanced industrial time series data.
Deep learning improves oilfield equipment maintenance and reduces downtime.
Study finds open data sets favor Western locales, impacting classifier performance.
Paper tackles adapting multiple domains to a target domain using distillation and dictionary learning.
Following our previous investigation of the USA Standard and Poor index anti-bubble that started in August 2000, we analyze thirty eight world stock market indices and identify 21 anti-bubble. An ``anti-bubble'' is defined as a self-fulfilling decreasing price created by positive price-to-price feedbacks feeding overal…
Study finds implicit government guarantee improves municipal investment bond ratings.
Adversarial attacks on spectrograms can fool audio classifiers trained on waveforms.
Using an analog of the boundary element method in engineering and science, we analyze and model unemployment rate in Austria, Italy, the Netherlands, Sweden, Switzerland, and the United States as a function of inflation and the change in labor force. Originally, the model linking unemployment to inflation and labor for…
Paper proposes semi-supervised learning for bearing anomaly detection.
The presence of log-periodic structures before and after stock market crashes is considered to be an imprint of an intrinsic discrete scale invariance (DSI) in this complex system. The fractal framework of the theory leaves open the possibility of observing self-similar log-periodic structures at different time scales.…
Author name disambiguation in bibliographic databases is the problem of grouping together scientific publications written by the same person, accounting for potential homonyms and/or synonyms. Among solutions to this problem, digital libraries are increasingly offering tools for authors to manually curate their publica…
Study improves precipitation predictions for High Mountain Asia using machine learning.
Being able to predict whether a song can be a hit has impor- tant applications in the music industry. Although it is true that the popularity of a song can be greatly affected by exter- nal factors such as social and commercial influences, to which degree audio features computed from musical signals (whom we regard as …
Paper proposes LIME for explaining machine learning credit risk predictions.
New method detects rock type changes in real-time during drilling.
Data describing historical growth of income per capita [Gross Domestic Product per capita (GDP/cap)] for the world economic growth and for the growth in Western Europe, Eastern Europe, Asia, former USSR, Africa and Latin America are analysed. They follow closely the linearly-modulated hyperbolic distributions represent…
Historical economic growth in Latin America is analysed using the data of Maddison. Unified Growth Theory is found to be contradicted by these data in the same way as it is contradicted by the economic growth in Africa, Asia, former USSR, Western Europe, Eastern Europe and by the world economic growth. Paradoxically, U…
This paper explores historical and philosophical aspects of angles and solid angles, inspired by Euler's work.
Quickly adapts fault diagnosis models for industrial machines.
Model shows worldwide trade crises can be localized or global, depending on trade balance.
Neural nets classify Thai Lukthung songs from other genres.
Study analyzes GDP growth of CEE countries using time-varying coefficients.
Modeling European spot power markets with game theory for Nash equilibria.
Study finds non-adherence to schizophrenia meds leads to earlier adverse events.
Improved visibility forecasts using statistical post-processing.
Study uses DMD to analyze oceanic features in Strait of Gibraltar.
The study uses neural networks to classify and predict coronavirus data.
Proposes a multi-objective variational autoencoder for smart infrastructure damage detection.
Combines GANs and EVT for better modeling of spatial climate extremes.
This paper presents the development of a hybrid learning system based on Support Vector Machines (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS) and domain knowledge to solve prediction problem. The proposed two-stage Domain Knowledge based Fuzzy Information System (DKFIS) improves the prediction accuracy attained…