The distribution of recurrence times or return intervals between extreme events is important to characterize and understand the behavior of physical systems and phenomena in many disciplines. It is well known that many physical processes in nature and society display long range correlations. Hence, in the last few year…
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48 results for “long time intervals”
Confidence Interval Construction for Multivariate time series using Long Short Term Memory Networkstat.ME
Paper proposes new method for time series confidence intervals using LSTM.
problem Constructing accurate confidence intervals for multivariate time series.
method Uses Long Short Term Memory Network (LSTM) and novel block bootstrap techniques.
result Demonstrates improved accuracy in constructing confidence intervals.
Efficiently predicts long-time dynamics of quantum spin models using MLP regression.
problem Challenges in calculating long-time expectation values for quantum spin models.
method Utilized a multi-layer perceptron (MLP) model for regression on matrix product states (MPS) expectation values.
result Significantly reduced computational cost for generating long-time dynamics while maintaining high accuracy.
BCI provides calibrated prediction intervals for time series forecasts.
problem Calibration of prediction intervals for time series forecasts.
method BCI wraps around any time series forecasting models and optimizes interval lengths using dynamic programming.
result BCI achieves long-term coverage under arbitrary distribution shifts and temporal dependence.
Energy markets and the associated energy futures markets play a crucial role in global economies. We investigate the statistical properties of the recurrence intervals of daily volatility time series of four NYMEX energy futures, which are defined as the waiting times between consecutive volatilities exceeding a gi…
It will be discussed the statistics of the extreme values in time series characterized by finite-term correlations with non-exponential decay. Precisely, it will be considered the results of numerical analyses concerning the return intervals of extreme values of the fluctuations of resistance and defect-fraction displa…
Proposes a matrix completion method for medical records with long time intervals.
problem Incomplete medical records due to long time intervals between patient visits.
method Decomposes a matrix with missing data into latent factors with locally linear constraint.
result The proposed algorithm achieves the best performance compared to existing methods.
We study the daily trading volume volatility of 17,197 stocks in the U.S. stock markets during the period 1989--2008 and analyze the time return intervals between volume volatilities above a given threshold q. For different thresholds q, the probability density function P_q(τ) scales with mean interval <τ> as P_q(τ…
An average instantaneous cross-correlation function is introduced to quantify the interaction of the financial market of a specific time. Based on the daily data of the American and Chinese stock markets, memory effect of the average instantaneous cross-correlations is investigated over different price return time inte…
Multiplicative Stochastic Model of the Time Interval between Trades in Financial Marketscond-mat.stat-mech
Stock price change in financial market occurs through transactions in analogy with diffusion in stochastic physical systems. The analysis of price changes in real markets shows that long-range correlations of price fluctuations largely depend on the number of transactions. We introduce the multiplicative stochastic mod…
The statistical properties of the bid-ask spread of a frequently traded Chinese stock listed on the Shenzhen Stock Exchange are investigated using the limit-order book data. Three different definitions of spread are considered based on the time right before transactions, the time whenever the highest buying price or th…
Understanding the statistical properties of recurrence intervals of extreme events is crucial to risk assessment and management of complex systems. The probability distributions and correlations of recurrence intervals for many systems have been extensively investigated. However, the impacts of microscopic rules of a c…
Ehrenfest Model with Large Jumps in Financecond-mat.stat-mech
Changes (returns) in stock index prices and exchange rates for currencies are argued, based on empirical data, to obey a stable distribution with characteristic exponent for short sampling intervals and a Gaussian distribution for long sampling intervals. In order to explain this phenomenon, an Ehrenfest model…
Study analyzes derivative-free loss method for solving PDEs and fluid problems.
problem Solving elliptic PDEs and fluid problems using neural networks.
method Derivative-free loss method with Feynman-Kac formulation and stochastic walkers.
result Training loss bias scales with time interval and spatial gradient, inversely with walker size.
Paper presents methods to create stock price confidence intervals using LSTM models.
problem Creating accurate confidence intervals for LSTM-estimated stock prices.
method Three bootstrap methods for dependent data, optimal block length selection, and benchmark comparison.
result Illustrated through stock price data, different bootstrap strategies provide varying confidence intervals.
We investigate the random walk of prices by developing a simple model relating the properties of the signs and absolute values of individual price changes to the diffusion rate (volatility) of prices at longer time scales. We show that this benchmark model is unable to reproduce the diffusion properties of real prices.…
Statistical Properties of Share Volume Traded in Financial Marketscond-mat.stat-mech
We quantitatively investigate the ideas behind the often-expressed adage `it takes volume to move stock prices', and study the statistical properties of the number of shares traded for a given stock in a fixed time interval . We analyze transaction data for the largest 1000 stocks for the two-year period 1…
PARNN improves ARNN with ARIMA feedback for accurate long-range forecasting.
problem Accurate long-range forecasting of complex time series data.
method Improves ARNN using ARIMA feedback, providing uncertainty quantification.
result PARNN outperforms state-of-the-art forecasters across various horizons.
Long-term relative arbitrage exists in markets where the excess growth rate of the market portfolio is bounded away from zero. Here it is shown that under a time-homogeneity hypothesis this condition will also imply the existence of relative arbitrage over arbitrarily short intervals.
The financial market is nonpredictable, as according to the Bachelier, the mathematical expectation of the speculator is zero. Nevertheless, we observe in the price fluctuations the two distinct scales, short and long time. Behaviour of a market in long terms, such as year intervals, is different from that in short ter…
Power Laws and Gaussians for Stock Market Fluctuationsphysics.soc-ph
The daily volume of transaction on the New York Stock Exchange and its day-to-day fluctuations are analysed with respect to power-law tails as well long-term trends. We also model the transition to a Gaussian distribution for longer time intervals, like months instead of days.
Study on flows in warped spaces without symmetry assumptions.
problem Analyzing curvature flows in non-symmetric warped spaces.
method Long-time existence and umbilicity estimates for flows with inverse speeds.
result Established long-time existence and umbilicity for flows in warped products.
Low-pass Recurrent Neural Networks - A memory architecture for longer-term correlation discoverycs.LG
A new memory strategy extends BPTT's learning window without longer traces.
problem Learning distant correlations in reinforcement learning requires efficient memory and learning strategies.
method Proposes a simple, effective memory strategy for extending BPTT's learning window.
result Extends BPTT's learning window without longer traces, improving efficiency.
A new LSTM model reduces state updates and improves convergence for long sequences.
problem Vanishing gradient problem in RNNs and slow convergence on long sequences.
method Gaussian-gated LSTM (g-LSTM) with a time gate to control neuron updates.
result The g-LSTM model reduces state updates and computes by at least 10x compared to an equivalent LSTM.
On non-markovian nature of stock tradingcond-mat.other
Using a relationship between the moments of the probability distribution of times between the two consecutive trades (intertrade time distribution) and the moments of the distribution of a daily number of trades we show, that the underlying point process generating times of the trades is an essentially non-markovian lo…
Gaussian Process Accelerated Feldman-Cousins Approach for Physical Parameter Inferencephysics.data-an
Efficiently accelerates Feldman-Cousins method using Gaussian processes.
problem Slow computation of confidence intervals in high-energy physics.
method Gaussian process acceleration of Feldman-Cousins method.
result Confidence intervals can be computed 5-10 times faster with similar accuracy.
Algorithm combines expert forecasts for long-term time series prediction.
problem Long-term time series prediction with expert advice.
method Develops algorithms to combine expert forecasts for long-term prediction, proving adversarial regret bounds.
result Obtains smoothing mechanism to protect against trend changes, noise, and outliers.
CPTD improves prediction intervals in time series regression with cross-sectional data.
problem Constructing valid prediction intervals in time series regression with a cross-section.
method Conformal Prediction with Temporal Dependence (CPTD) for post-hoc, light-weight approach.
result CPTD maintains cross-sectional validity while improving longitudinal coverage.
The paper explores how market trade values and volumes affect price and return statistics.
problem Understanding the statistical properties of market trade, price, and return.
method Introduces secondary averaging procedure to describe statistical moments of market trades, price, and return.
result Predictions of market-based probabilities of price and return are limited by Gaussian distributions.
Long Memory in Stock Tradingcond-mat.stat-mech
Using a relationship between the moments of the probability distribution of times between the two consecutive trades (intertrade time distribution) and the moments of the distribution of a daily number of trades we show, that the underlying point process is essentially non-markovian. A detailed analysis of all trades i…
New CTRW model explains volatility clustering in stock markets.
problem Missing models for long-term memory in time intervals between observations.
method Introduced a new family of CTRWs with correlated waiting times.
result Successfully describes the decay of nonlinear autocorrelation function in stock market returns.
DeepAries optimizes rebalancing intervals and asset allocations for better portfolio performance.
problem Fixed rebalancing intervals lead to unnecessary transactions and poor risk-adjusted returns.
method Adaptive deep reinforcement learning with Transformer state encoder and PPO.
result DeepAries outperforms traditional strategies in risk-adjusted returns, transaction costs, and drawdowns.
LSTM improves cross-network recommendations by capturing user preference changes and irregular time intervals.
problem Offline cross-network recommender solutions fail to capture user preference changes and dynamic environments.
method Proposes a multi-layered LSTM network with attention mechanisms, higher order interactions, and time-aware gates.
result The model consistently outperforms state-of-the-art in accuracy, diversity, and novelty.
This paper presents an empirical investigation of the intraday Brazilian stock market price fluctuations, considering q-Gaussian distributions that emerge from a non-extensive statistical mechanics. Our results show that, when returns are measured over intervals less than one hour, the empirical distributions are well …
Enhanced TSFMs improve time series forecasting accuracy and reliability.
problem Variance, bias, and uncertainty in TSFMs' predictions on real data.
method Statistical and ensemble techniques including bagging, stacking, residual modeling, and prediction intervals.
result Hybrid models consistently outperform standalone TSFMs across multiple horizons.
By applying the multifractal detrended fluctuation analysis to the high-frequency tick-by-tick data from Deutsche Börse both in the price and in the time domains, we investigate multifractal properties of the time series of logarithmic price increments and inter-trade intervals of time. We show that both quantities rev…
In order to investigate the origin of large price fluctuations, we analyze stock price changes of ten frequently traded NASDAQ stocks in the year 2002. Though the influence of the trading frequency on the aggregate return in a certain time interval is important, it cannot alone explain the heavy tailed distribution of …
Rough Transformers improve efficiency for medical time-series data.
problem Efficiently modeling irregularly sampled, long-range time-series data.
method Introducing Rough Transformers, a Transformer variant with continuous-time representations and multi-view signature attention.
result Rough Transformers outperform vanilla Transformers while using less computational resources.
This paper deals with dependence across marginally exponentially distributed arrival times, such as default times in financial modeling or inter-failure times in reliability theory. We explore the relationship between dependence and the possibility to sample final multivariate survival in a long time-interval as a sequ…
We study the return interval between price volatilities that are above a certain threshold for 31 intraday datasets, including the Standard & Poor's 500 index and the 30 stocks that form the Dow Jones Industrial index. For different threshold , the probability density function scales with the mean i…
We construct a finitely presented group with non-quadratic Dehn function majorizable by a quadratic function on arbitrary long intervals.
The statistical properties of the return intervals between successive 1-min volatilities of 30 liquid Chinese stocks exceeding a certain threshold are carefully studied. The Kolmogorov-Smirnov (KS) test shows that 12 stocks exhibit scaling behaviors in the distributions of for different thresholds . …
New method assesses prediction intervals across different operating points.
problem Difficulty in comparing prediction intervals across studies.
method Operating characteristics curves and gain over a simple reference.
result A novel operating point agnostic assessment methodology for prediction intervals.
Researchers have studied the first passage time of financial time series and observed that the smallest time interval needed for a stock index to move a given distance is typically shorter for negative than for positive price movements. The same is not observed for the index constituents, the individual stocks. We use …
Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short an…
Neural ARFIMA model improves exchange rate forecasting for BRIC economies.
problem Forecasting exchange rates for emerging markets with long-term memory and nonlinear dynamics.
method Integrates ARFIMA for long-memory with neural networks for nonlinear approximation.
result NARFIMA model outperforms benchmarks in BRIC exchange rate forecasting.
We investigate the probability distributions of the recurrence intervals between consecutive 1-min returns above a positive threshold or below a negative threshold of two indices and 20 individual stocks in China's stock market. The distributions of recurrence intervals for positive and negative thresho…
We investigate the probability distribution of the volatility return intervals for the Chinese stock market. We rescale both the probability distribution and the volatility return intervals as to obtain a uniform scaling curve for different threshold value . The scali…