Study on time-zero efficiency of European power derivatives markets using statistical tests and trading rules.
problem Assessing time-zero efficiency in European power derivatives markets.
method Statistical tests based on the law of one price and trading rules based on price differentials and no-arbitrage violations applied to daily data of three European power markets.
result Definite conclusions on time-zero efficiency are not possible for French and Spanish markets due to liquidity and representativeness challenges.
Study shows cryptocurrency market efficiency changes over time.
problem Measuring cryptocurrency market efficiency over time.
method Used a generalized least squares-based time-varying model to measure efficiency without sample size dependence.
result Bitcoin's market efficiency is higher than Ethereum's over most periods.
Cryptocurrencies show varying levels of efficiency over time, forming clusters with younger ones mimicking older ones.
problem Determining the efficiency of cryptocurrencies over time.
method Permutation entropy and statistical complexity over sliding time-windows of price log returns.
result 37% of cryptocurrencies are efficient over 80% of the time, while 20% are efficient in less than 20% of the time.
A new LSTM architecture improves time series forecasting efficiency.
problem Efficiency and accuracy in time series forecasting using linear models.
method Attention-free LSTM architecture for time series prediction.
result Improved prediction capacity and efficiency compared to LSTM.
Study finds time-varying volatility and multifractality in Bitcoin, with asymmetry weakening as market efficiency increases.
problem Investigating time-varying properties of Bitcoin's volatility and multifractality.
method Rolling window method to examine daily Bitcoin returns and multifractal properties over time.
result Volatility asymmetry in Bitcoin changes over time, becoming less pronounced as market efficiency increases.
NEMoTS improves time series analysis by deriving efficient, interpretable models.
problem Lack of comprehensive understanding and insightful explanations in time series analysis.
method Neural-enhanced Monte-Carlo Tree Search (NEMoTS) for symbolic regression.
result NEMoTS provides efficient and interpretable models for time series analysis.
This study examines Bitcoin's market efficiency, liquidity, and multifractality over time.
problem Investigating market efficiency, liquidity, and multifractality of Bitcoin.
method Dynamic analysis of Bitcoin's time series data, including Hurst exponent and multifractal degree.
result Market efficiency improves as liquidity increases, but multifractality remains anti-persistent.
This paper develops a non-Bayesian methodology to analyze the time-varying structure of international linkages and market efficiency in G7 countries. We consider a non-Bayesian time-varying vector autoregressive (TV-VAR) model, and apply it to estimate the joint degree of market efficiency in the sense of Fama (1970, 1…
Study shows stock market efficiency varies over time and can be networked.
problem Understanding the dynamic and collective aspects of stock market efficiency.
method Defined and calculated time-varying efficiency using permutation entropy of log-returns.
result Major world stock markets can be hierarchically classified into groups with similar efficiency profiles, but these rankings are unstable.
Efficient deep policy gradient method for continuous-time control problems.
problem Optimal control in continuous time with fine time discretization.
method Multi-scale deep policy gradient method with varying time discretization.
result Targeted efficiency in computational resources achieved through multi-scale approach.
The discrete-time mean-variance portfolio selection formulation, a representative of general dynamic mean-risk portfolio selection problems, does not satisfy time consistency in efficiency (TCIE) in general, i.e., a truncated pre-committed efficient policy may become inefficient when considering the corresponding trunc…
Efficient decoder design improves semantic segmentation for real-time automated driving.
problem Efficient design of decoders for real-time semantic segmentation in automated driving.
method Design of a novel non-bottleneck layer and family of decoders using VGG10 as an efficient encoder.
result Improvement of 10% in performance compared to a baseline.
A non-Bayesian time-varying model is developed by introducing the concept of the degree of market efficiency that varies over time. This model may be seen as a reflection of the idea that continuous technological progress alters the trading environment over time. With new methodologies and a new measure of the degree o…
Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations. The recent pruning based work ESE suffers from degradation of performance/energy efficiency due to the irregular network structure after pruning. We propose bl…
Paper proposes an EKF for estimating time-varying market efficiency.
problem Estimating time-varying market efficiency under nonlinear dynamics.
method Extended Kalman Filter (EKF) for time-varying autoregressive models.
result U.S. market generally remained weak-form efficient since mid-1946.
Study shows using time-series privileged information improves model efficiency.
problem Efficiently predicting future outcomes using supervised models with privileged information.
method Developed an algorithm for learning with privileged time-series data and proved its efficiency for non-stationary Gaussian-linear systems.
result Learning with privileged information is more efficient than without it for non-stationary Gaussian-linear systems.
Study finds varying market efficiency in prewar and wartime Japanese stock market.
problem Measuring market efficiency in prewar and wartime Japanese stock market.
method Using a new market capitalization-weighted stock price index, the study examines market efficiency over time and historical events.
result The adaptive market hypothesis is supported in the prewar and wartime Japanese stock market, with efficiency varying over time and with historical events.
Study shows how market efficiency changes during the pandemic.
problem Understanding market efficiency during the pandemic.
method Applied time-varying vector autoregression model.
result Market efficiency changes over time and can be improved by enhanced linkages.
The paper optimizes RV estimation by efficient sampling in time-changed diffusion models.
problem Improving realized variance (RV) estimation in time-changed diffusion models.
method Theoretical analysis and simulations of hitting time and realized business time sampling schemes.
result Realized business time sampling is empirically most efficient for high noise levels.
New method estimates causal effects with multi-valued, time-varying treatments.
problem Estimating causal effects with complex time-varying exposures.
method Combines machine learning and semiparametric efficiency theory.
result Proposes an efficient, asymptotically normal estimator for marginal structural models.
This study evaluates prewar Japanese financial market efficiency using time-varying models.
problem Determining when prewar Japanese financial market lost its price formation function.
method Time-varying parameter model, generalized least squares-based time-varying vector autoregressive model.
result The prewar Japanese financial market lost its price formation function in 1932.
Improves diffusion model performance and efficiency through classical search.
problem Tackles inference-time control in diffusion models.
method Proposes a framework combining local and global search for efficient navigation.
result Significant gains in performance and efficiency across various domains.
QABBA improves time series storage efficiency while preserving shape information.
problem Efficient storage and shape preservation of time series data.
method Quantized symbolic time series approximation (QABBA) using ABBA technique.
result QABBA achieves a new state-of-the-art on Monash regression dataset.
SEFR is a fast, energy-efficient classifier for ultra-low power devices.
problem Running machine learning on battery-powered devices is challenging due to time and energy constraints.
method SEFR is an ultra-low power classifier with linear time complexity for training and testing.
result SEFR is 63 times faster and 70 times more energy efficient than state-of-the-art classifiers.
Statistical test rejects market efficiency using entropy from price returns.
problem Determining market efficiency using information theory.
method Symbolic representation of price returns, Shannon entropy, and statistical test.
result Rejects market efficiency hypothesis for various datasets.
Faster policy learning via continuous-time gradients.
problem Efficiently estimating policy gradients for continuous-time systems.
method Approximating continuous-time gradients directly, using adaptive discretization.
result More efficient policy gradient estimator leads to faster learning.
New algorithm for clustering with faulty oracle achieves optimal queries and efficiency.
problem Clustering with a faulty oracle, especially for multiple clusters.
method Built on stochastic block model, provides nearly-optimal query complexity.
result Time-efficient algorithm with nearly-optimal query complexity for all constant k and any δ.
We investigated financial market data to determine which factors affect information flow between stocks. Two factors, the time dependency and the degree of efficiency, were considered in the analysis of Korean, the Japanese, the Taiwanese, the Canadian, and US market data. We found that the frequency of the significant…
Paper presents a copula-based method to efficiently generate correlated sample paths from multi-step time series models.
problem Generating realistic correlation structures in multi-step forecast sample paths is expensive and time-consuming.
method Copula-based approach to generate correlated sample paths in one forward pass.
result Improved sample path quality and significant speedup over autoregressive sampling.
DeepESNs outperform ESN and GRUs in multivariate time-series prediction.
problem Comparing DeepESNs and gated RNNs for multivariate time-series prediction.
method Experimental comparison of DeepESNs and gated RNNs (Gated Recurrent Units, Long Short-Term Memory) on 4 polyphonic music tasks.
result DeepESNs outperform ESN and GRUs in terms of prediction accuracy and efficiency.
FPGA-based logic architecture speeds up GBDT training 259x.
problem Training efficiency and power consumption in GBDT models.
method Implemented logic architecture on FPGA, compared with software libraries.
result Training speed 26-259x faster, power efficiency 90-1,104x higher.
New linear models improve time series classification efficiency and interpretability.
problem Complex and inefficient classifiers limit interpretability and applicability to variable-length time series.
method Symbolic representations, multi-resolution, multi-domain, linear models.
result mtSS-SEQL+LR achieves similar accuracy to state-of-the-art methods but with lower time and memory usage.
Efficiently estimates binary product distributions with privacy.
problem Estimating means of binary product distributions privately and accurately.
method Polynomial time, pure differential privacy approach.
result Optimal sample complexity with polylogarithmic factors.
This study examines the adaptive market hypothesis (AMH) in Japanese stock markets (TOPIX and TSE2). In particular, we measure the degree of market efficiency by using a time-varying model approach. The empirical results show that (1) the degree of market efficiency changes over time in the two markets, (2) the level o…
The Moscow Stock Exchange was inefficient for most of 2012-2021.
problem Measuring market efficiency of the Moscow Stock Exchange.
method Filtering out regularities, calculating Shannon entropy, clustering returns, using Monte Carlo simulations.
result The Moscow Stock Exchange was inefficient for most of 2012-2021.
SeFT classifies irregular time series with high scalability and data efficiency.
problem Classifying irregularly-sampled time series with unaligned measurements.
method SeFT (Set Functions for Time Series) based on differentiable set function learning, extremely parallelizable.
result SeFT performs competitively with healthcare time series datasets and significantly reduces runtime.
CauScale efficiently discovers causal relationships in large graphs.
problem Efficiency bottlenecks in causal discovery for large graphs.
method Neural architecture with reduction unit and tied attention weights.
result Achieves 99.6% mAP on in-distribution data and 84.4% on out-of-distribution data.
We investigate the relationship between market efficiency of rice futures transaction in Osaka and the Japanese government intervention in rice distributions by directly buying and selling rice during the interwar period, from the middle 1910s to 1939, considering the context of "discretion versus rules." We use a time…
OneShotSTL efficiently decomposes time series online, improving speed and accuracy.
problem Real-time analysis of time series data with low processing delay.
method Online seasonal-trend decomposition algorithm with O(1) update time complexity.
result 1,000 times faster than batch methods with comparable accuracy.
Study improves sampling efficiency of diffusion models using RL and PDEs.
problem Training neural stochastic differential equations without access to target samples.
method Proves equivalences between RL methods and PDEs, uses coarse time discretization.
result Improves sample efficiency and reduces computational cost.
Efficient method for lookback option pricing under Markov models.
problem Pricing lookback options under Markov models.
method Model-free representations combined with numerical quadrature and Markov chain approximation.
result Efficient method applicable to various Markov models.
Efficiently tunes hyperparameters for online traffic time series prediction.
problem Online hyperparameter tuning for machine learning models in time series prediction.
method Online hyperparameter optimization algorithm for Kernel Ridge regression.
result Achieves better or similar prediction accuracy with significantly less computation time.
CARDS improves decoding efficiency and alignment quality for LLMs.
problem Efficiency bottlenecks in decoding-time alignment for LLMs.
method Cascade Reward Sampling (CARDS) with segment-level rejection sampling and uncertainty-based segmentation.
result Significant improvement in decoding efficiency and alignment quality.
Linformer reduces transformer complexity to linear, improving efficiency.
problem High cost of training and deploying large transformer models for long sequences.
method Approximates self-attention with low-rank matrix, proposing Linformer with O(n) complexity. result Linformer performs similarly to standard transformers but is more memory- and time-efficient.
sktime toolkit benchmarks time series classification algorithms for correctness and efficiency.
problem Benchmarking correctness and efficiency of time series classification algorithms.
method Implementation and comparison of six classifiers in sktime with their tsml equivalents.
result Significant differences in accuracy and efficiency between algorithms, with one causing debugging issues.
New insights into RL efficiency from managing time discretization.
problem The impact of time discretization on RL methods in continuous-time systems.
method Analysis of Monte-Carlo policy evaluation for LQR systems.
result An optimal choice of temporal resolution for a given data budget improves policy evaluation efficiency.
This work identifies eigenvalues of unknown linear dynamics without full system identification.
problem Identifying parameters of a linear dynamical system is challenging.
method Developed a computationally efficient algorithm to estimate eigenvalues of the state-transition matrix.
result The algorithm can efficiently cluster multi-dimensional time series with temporal offsets and varying lengths.
Paper tackles efficient off-policy evaluation in long-horizon settings.
problem Efficient off-policy evaluation in long-horizon settings with diminishing overlap.
method Derives efficiency bounds for OPE under Markovian and time-invariant structures, develops a new DRL estimator.
result DRL estimator provides efficient OPE even with just one dependent trajectory in time-invariant Markov decision processes.