Proposes a new neural network system for improved reasoning and learning.
problem Limited prediction horizon and lack of accurate learning algorithms in FCMs.
method Introduces Short-term Cognitive Networks with non-constricted weights and a nonsynaptic learning algorithm.
result Improves network performance and prediction accuracy without modifying existing weights.
This paper proposes a framework to predict long-term trends and short-term fluctuations in multivariate time series.
problem Existing prediction methods often ignore the distinction between long-term trends and short-term fluctuations.
method The paper introduces a MTS forecasting framework that uses both original time series and its first difference to capture long-term trends and short-term fluctuations.
result The proposed method improves forecasting performance by using more supervision information.
Many different classification tasks need to manage structured data, which are usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that the vertices/edges of each graph may change during time. Our goal is to jointly exploit structured data and temporal information through the use of a neural networ…
New LSTM scheme incorporates prior knowledge and measurement uncertainties.
problem Overfitting and insufficient data for accurate time-dependent solutions.
method Sparse Bayesian training algorithm for automatic connection determination.
result Less prone to overfitting, smaller data set required for satisfying accuracy.
Predicting and improving player retention is crucial to the success of mobile Free-to-Play games. This paper explores the problem of rapid retention prediction in this context. Heuristic modeling approaches are introduced as a way of building simple rules for predicting short-term retention. Compared to common classifi…
System identifies language of transliterated text.
problem Users struggle to understand non-native language transliterated text.
method Feature extraction of phonetic syllables using LSTM network.
result System accurately identifies language of transliterated text.
Paper uses LSTM neural networks to forecast commodity prices.
problem Forecasting accuracy of traditional methods like ARIMA.
method Long Short-Term Memory (LSTM) neural networks complement traditional methods.
result Forecast averaging of LSTM and ARIMA models improves forecast accuracy.
Paper compares GRU and LSTM for predicting wildfire spread direction.
problem Predicting wildfire spread direction with limited data.
method Comparison of Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks.
result GRU performs better for longer time series than LSTM.
Develops LSTM for predicting neuronal dynamics over long time-horizons.
problem Understanding and controlling complex brain behaviors.
method Long Short-Term Memory (LSTM) neural network architecture for multi-time step predictions.
result LSTM improves short time-horizon prediction accuracy and multi-time step predictions of neuronal dynamics.
Bayesian model predicts interest rates with short-term accuracy and long-term stability.
problem Improving short- and long-term prediction of time series with temporary non-stationary behavior.
method Time-varying autoregressive model with Bayesian regularization and MCMC inference.
result Model outperforms existing methods in both short and long-term predictions.
Short-term trend-following has stopped delivering profits since 2009, especially on smaller market ticks.
problem The profitability of short-term trend-following has declined since 2009.
method Cross-sectional analysis of 100 liquid futures contracts from 1995-2025, evaluating four explanations.
result The decline in short-term trend-following profits is linked to smaller market ticks, not asset class or liquidity.
Novel CMG framework improves financial sentiment forecasting.
problem Challenges in short-term sentiment forecasting of financial OHLC data.
method Integrates chaos theory, Markov chains, and Gaussian processes with transformer models.
result Consistently outperforms traditional models in accuracy and efficiency.
LSTM models outperform traditional ARIMA in S&P 500 forecasting.
problem Forecasting volatile financial data with non-linear dependencies.
method Compared LSTM and ARIMA models using historical data and technical indicators.
result LSTM models outperformed ARIMA in accuracy and error metrics.
Modeling physiological time-series in ICU is of high clinical importance. However, data collected within ICU are irregular in time and often contain missing measurements. Since absence of a measure would signify its lack of importance, the missingness is indeed informative and might reflect the decision making by the c…
Paper predicts future vehicle trajectories for safer AVs.
problem Improving accuracy of long-term vehicle trajectory prediction for autonomous vehicles.
method Dual LSTM network for automatic learning of driver behaviors and future trajectory prediction.
result The method achieves lower RMSE for longitudinal and lateral predictions compared to state-of-the-art methods.
Model combines long-term and short-term memory using conceptors.
problem Transfer between long-term and short-term memory.
method Recurrent neural network with gated reservoir for short-term memory and conceptors for long-term memory.
result Standard operations on conceptors allow combining long-term memories and describing their effect on short-term memory.
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.
This paper combines LLMs with RL for better trading strategies.
problem Myopic behavior and opaque policies in RL for trading.
method LLMs generate strategic trading advice to guide RL agents.
result LLM-guided RL agents outperform unguided RL in return and risk metrics.
Investment strategies derived from commodity futures curves exploit dynamics in price movements.
problem Modeling and predicting the term structure of commodity futures prices.
method Employed the Nelson-Siegel framework to model term structure, and developed investment strategies based on changes in slope and curvature parameters.
result Significant profits generated from systematic strategies based on the change in slope, unrelated to risk factors and robust to transaction costs.
Paper optimizes recommendation systems for long-term business metrics.
problem Short-term reward optimization ignores long-term business metrics.
method Introduced a framework for modeling long-term rewards in RecoGym.
result Proposed a simple extension leading to state-of-the-art results.
The paper identifies short-term and long-term time scales in stock markets with and without structural breaks.
problem Understanding the nature of stock markets at short-term and long-term time scales.
method Applied Zivot and Andrews structural trend break model to identify structural breaks. Used empirical mode decomposition and Hurst exponent to analyze time scales.
result Identified short-term and long-term time scales in stock markets, with short-term scales within few days to 3 months and long-term scales greater than 5 months.
Investigation shows CTAs and STRTs can benefit from coexistence.
problem CTAs' poor performance in recent years.
method Built an artificial market model with CTAA and STRTA agents.
result CTAA and STRTA coexistence leads to higher trading and revenue.
QLSTM outperforms LSTM in predicting KSE 100 index movements.
problem Predicting stock market movement in uncertain economic conditions.
method Used LSTM and QLSTM models on monthly data of economic indicators.
result QLSTM provided more accurate predictions of KSE 100 index values.
CONTINA provides adaptive confidence intervals for traffic demand prediction.
problem Uncertainty in future traffic demand predictions and the need for valid confidence intervals.
method Adaptive confidence interval method that adjusts based on deployment errors.
result Valid confidence intervals with shorter lengths and theoretical coverage guarantee.
Kernel method estimates long-term effects from short-term data.
problem Estimating long-term effects from short-term data in continuous actions.
method Kernel ridge regression to embed and extrapolate long-term effects.
result Uniform consistency and nonasymptotic error bounds for the estimator.
Study shows risk-averse investors have consistent ranking of risky assets.
problem Ranking of risky assets in short-term investments.
method Analyzes various decision problems regarding risky assets with continuous returns.
result Risk-averse decision makers have the same ranking over risky assets.
Predicts short-term futures contract direction using neural networks and order flow data.
problem Challenges in predicting short-term directional movement of futures contracts.
method Engineering features from technical analysis, order flow, and order-book data; training a Tabnet neural network.
result Achieved an accuracy of 0.601 in predicting directional change on the Silver Futures Contract.
A new model for pricing ultra-short-term options with complex volatility patterns.
problem Complex pricing of ultra-short-term options due to oscillations in implied volatility.
method Edgeworth++ model with nonparametric stochastic volatility and deterministic shift extension.
result Fast and accurate closed-form option pricing for ultra-short-term options.
Statistical models outperform mechanistic models in short-term COVID-19 incidence forecasts.
problem Comparing accuracy of mechanistic vs statistical models for short-term COVID-19 incidence forecasts.
method Empirical comparison of forecasts from mechanistic and statistical models using daily incidence data from six US states.
result Statistical models are at least as accurate as mechanistic models and better capture volatility.
TimeMixer predicts global financial asset volatility, excelling in short-term forecasts.
problem Predicting volatility in global financial markets is challenging due to complexity and non-linear dynamics.
method Uses TimeMixer, a multiscale-mixing model for forecasting across different scales.
result TimeMixer performs exceptionally well in short-term volatility forecasting but less so in longer-term predictions.
{\em Riemannian cubics} are curves in a manifold M that satisfy a variational condition appropriate for interpolation problems. When M is the rotation group SO(3), Riemannian cubics are track-summands of {\em Riemannian cubic splines}, used for motion planning of rigid bodies. Partial integrability results are know…
Study predicts US stock market will continue to fall post-COVID-19.
problem Analyzing the recovery trend of the US stock market post-COVID-19.
method Used Deep Learning, Neuro Network, and Time-series analysis on S&P 500, Nasdaq 100, and Dow Jones Industrial Average data.
result LSTM model predicts US stock market will continue to fall post-COVID-19.
This paper balances short-term and long-term rewards in policy learning.
problem Balancing short-term and long-term rewards in policy learning.
method Formalizes a new framework to balance rewards, identifies rewards under mild assumptions, deduces efficiency bounds, and develops a policy learning approach.
result The proposed method improves the estimator of long-term reward and reduces regret.
Paper proposes KSHMM for short-term wind-speed forecasting.
problem Short-term wind-speed prediction challenge.
method Kernel Spectral Hidden Markov Model (KSHMM) for time series forecasting.
result KSHMM-based technique offers comparable or better performance than other methods.
NeuralHydrology uses LSTMs to forecast rainfall-runoff, revealing interpretable patterns.
problem Difficulty in interpreting LSTMs in environmental sciences.
method Application of LSTMs for rainfall-runoff forecasting in hydrology, analyzing patterns internally.
result Trained LSTMs reveal patterns consistent with hydrological system understanding.
RNNs excel at both short-term and long-term user interaction prediction.
problem Modeling user preferences over short and long time horizons.
method Evaluating RNN-based models on both short-term and long-term recommendation tasks.
result RNNs can predict immediate and distant user interactions.
CP provides reliable prediction intervals for short-term power markets.
problem Short-term electricity price forecasting in power markets.
method Conformal Prediction (CP) integrated with various point forecast models.
result CP yields sharp and reliable prediction intervals in short-term power markets.
New framework estimates long-term outcomes from short-term data.
problem Estimating long-term outcomes from short-term data.
method Reward function decomposition-based framework (LOPE).
result LOPE outperforms existing methods, especially when surrogacy is violated.
Comparative study of neural networks for short-term FOREX forecasting.
problem Simulating expert judgment in foreign exchange market forecasting.
method Implemented and compared LSTM and ANN architectures for short-term FOREX forecasting.
result ANN custom architecture outperforms LSTM in prediction quality and resource efficiency.
Long short-term memory network outperforms seasonal model in JSE Top 40 forecasting.
problem Comparing neural network performance to traditional models in financial forecasting.
method Used long short-term memory network for JSE Top 40 return data forecasting.
result Long short-term memory network outperforms seasonal model in forecasting.
TimeBridge addresses non-stationarity in long-term time series forecasting.
problem Non-stationarity in multivariate time series leads to spurious regressions and obscures long-term relationships.
method TimeBridge segments series into patches, applying Integrated Attention for short-term non-stationarity and Cointegrated Attention for long-term cointegration.
result TimeBridge achieves state-of-the-art performance in both short-term and long-term forecasting.
QLSTM improves speech recognition by considering internal quaternion dependencies.
problem Weak internal dependencies in traditional RNNs for speech recognition.
method Proposes QLSTM, a quaternion-based LSTM that considers both external and internal dependencies.
result QLSTM achieves better performance with up to 2.8 times fewer parameters.
Established a relation between short-term and long-term arbitrage measures.
problem Non-equilibrium effects in financial markets.
method Geometric Arbitrage Theory and Stochastic Portfolio Theory.
result A connection between short-term and long-term arbitrage measures.
Proposes LSR-IGRU for improved stock trend prediction.
problem Challenges in stock price prediction due to complex relationships and nonlinear dynamics.
method Long short-term relationships matrix and improved GRU input for better temporal and relationship integration.
result Significantly improved accuracy in predicting stock trend changes.
Study finds short-term wage increases due to COVID-19, contrary to expectations.
problem Impact of COVID-19 on wages over time.
method Empirical analysis controlling for GDP as a demand proxy.
result Short-term positive wage effect, contrary to expectations.
Study finds short-term trading signals can enhance alpha in U.S. S&P 500 portfolios.
problem Traditional factor investing misses real-time market dislocations.
method Double-selection LASSO framework to control for fundamental factors and isolate trading signals.
result 17 distinct trading signals capture significant risk premiums and enhance portfolio diversification.
Unified model predicts equipment failure and remaining useful life.
problem Predicting equipment failure and remaining useful life separately is sub-optimal.
method Two methods: Deep Weibull model (DW-RNN) and multi-task learning (MTL-RNN).
result Our methods consistently outperform baseline RUL methods and produce consistent results for RUL and FP.
Model predicts short-term Amazon rainforest fires with high accuracy.
problem Accurate short-term forecasting of Amazon rainforest fires is challenging.
method Used Seasonal and Trend decomposition based on Loess combined with multi-month-ahead load forecasting algorithms.
result Proposed decomposition-ensemble models provide more accurate forecasts than other models.