Deep learning methods improve time series forecasting by optimizing lag selection.
problem Optimizing the number of lags for accurate univariate time series forecasting.
method Empirical analysis of deep learning methods trained on multiple time series datasets.
result Excessively small or large lag sizes negatively impact forecasting performance.
We develop methods to estimate lag and parameters for multiple stable autoregressive processes.
problem Estimating lag and parameters for multiple stable autoregressive processes with unknown lag.
method Use convex programming to simultaneously select lag and estimate parameters across multiple processes.
result The estimated process is stable, and forecasting errors can outperform known rates.
Bayesian framework selects features and lags for time series forecasting.
problem Variable selection and lagged error term identification in time series models.
method Hierarchical Bayesian models with spike-and-slab priors, two-stage MCMC algorithm.
result Posterior selection consistency under mild conditions, improved predictive performance.
Vector autoregression (VAR) is a fundamental tool for modeling multivariate time series. However, as the number of component series is increased, the VAR model becomes overparameterized. Several authors have addressed this issue by incorporating regularized approaches, such as the lasso in VAR estimation. Traditional a…
Pairs Trading is carried out in the financial market to earn huge profits from known equilibrium relation between pairs of stock. In financial markets, seldom it is seen that stock pairs are correlated at particular lead or lag. This lead-lag relationship has been empirically studied in various financial markets. Earli…
New algorithm reduces adaptation lag in online model selection.
problem Adaptation lag in online model selection for non-stationary environments.
method Optimistic online mirror descent with safeguarded large learning rates.
result Reduces adaptation lag from hundreds of rounds to a few rounds.
Variable selection in linear models plays a pivotal role in modern statistics. Hard-thresholding methods such as l0 regularization are theoretically ideal but computationally infeasible. In this paper, we propose a new approach, called the LAGS, short for "least absulute gradient selector", to this challenging yet i…
Expert system predicts credit card charge-offs using macroeconomic indicators.
problem Managing charge-off rates in the credit card industry.
method Developed an expert system using machine learning and macroeconomic indicators.
result Achieved mean squared error values of 1.15E-03 and 1.04E-03.
DCIts interprets complex time series data with interpretable coefficients.
problem Interpreting nonlinear multivariate time series data.
method Deep convolutional architecture with a Focuser and Modeler components.
result DCIts provides interpretable coefficients and interaction patterns.
To reduce the long training time of large deep neural network (DNN) models, distributed synchronous stochastic gradient descent (S-SGD) is commonly used on a cluster of workers. However, the speedup brought by multiple workers is limited by the communication overhead. Two approaches, namely pipelining and gradient spar…
One popular approach for nonstructural economic and financial forecasting is to include a large number of economic and financial variables, which has been shown to lead to significant improvements for forecasting, for example, by the dynamic factor models. A challenging issue is to determine which variables and (their)…
Proposes a method for forecasting time series with multiple seasonality.
problem Forecasting time series with both short-term and long-term seasonality is challenging.
method Two-stage method: first generalizes ARMA model for multiple seasonality, second selects lag order.
result Method outperforms `Facebook Prophet` model in predictive performance.
New neural network models improve Granger Causality detection in non-linear systems.
problem Mischaracterization of Granger Causality in non-linear systems using traditional linear models.
method Proposes Learned Kernel VAR (LeKVAR) and decoupled penalties for GC estimation and lag selection.
result Improves GC detection in non-linear systems with computational efficiency.
A new method selects robust features for ML models using causal discovery.
problem Challenges in feature selection for ML models with limited domain knowledge.
method Multidata causal feature selection using PC1 or PCMCI algorithms.
result The method improves model performance and provides interpretable drivers.
LASG improves communication efficiency in distributed learning.
problem Efficiently communicating gradients in distributed machine learning.
method Develops a new stochastic gradient descent approach, LASG, that predicts and selects significant communication rounds.
result Achieves communication savings by an order of magnitude in federated learning.
Algorithm detects lead-lag relationships in multivariate time series.
problem Understanding temporal dependencies between time series.
method Cluster-driven methodology based on dynamic time warping.
result Robust detection of lead-lag relationships in lagged multi-factor models.
Detects lead-lag clusters in US equity market time series.
problem Identifying lead-lag relationships in multivariate time series.
method Directed network clustering of lead-lag relationships.
result Validated on US equity market data, detects statistically significant lead-lag clusters.
The study extracts market direction from transaction data.
problem Extracting market direction from transaction data.
method Dynamic equation with time scale selection from past transactions.
result Automatic determination of time scale for price calculation.
A HMM for intraday momentum trading reduces lagging and incorporates side information.
problem Time-lagging in existing momentum trading models leads to incorrect momentum signals.
method State space formulation with latent momentum states, cross-validation for state estimation, and Bayesian inference for prediction.
result The model reduces lagging and accurately predicts market changes.
DOLCE improves off-policy evaluation and learning by decomposing effects.
problem Bias in off-policy evaluation and learning due to policy mismatch.
method Uses lagged contexts and a moment-based training procedure to decompose and cancel bias.
result DOLCE achieves substantial improvements in off-policy evaluation and learning.
NAPLES resolves lead-lag analysis challenges in non-synchronous high-frequency data.
problem Challenges in analyzing lead-lag effects due to non-synchronous observations and high-frequency data.
method NAPLES (Negative And Positive lead-lag EStimator) resolves these challenges.
result NAPLES has a strong correlation with actual lead-lag effects, including those triggered by macroeconomic announcements.
Method detects lead-lag relationships in multivariate time series.
problem Discovering lead-lag relationships in multivariate time series.
method Clustering-driven methodology using sliding window and various clustering techniques.
result Robust lead-lag estimates across clusters enhance consistent relationships identification.
Modeling delayed Granger causality in Hawkes processes.
problem Capturing the time lag between causal events in multivariate Hawkes processes.
method Proposed a Hawkes process model with latent time lags, using Variational Auto-Encoder (VAE) for inference.
result Identified and inferred time lags with posterior distributions, improving event prediction and root cause analysis.
The study finds that factor momentum is significant only at short lags compared to stock momentum.
problem Investigating the relationship between factor momentum and stock momentum.
method Replicated earlier findings and conducted a spanning test controlling for stock momentum and factor exposure.
result Factor momentum is significant only at short lags after controlling for stock momentum and factor exposure.
The existence of time-lagged cross-correlations between the returns of a pair of assets, which is known as the lead-lag relationship, is a well-known stylized fact in financial econometrics. Recently some continuous-time models have been proposed to take account of the lead-lag relationship. Such a model does not follo…
We propose a novel framework to investigate lead-lag relationships between two financial assets. Our framework bridges a gap between continuous-time modeling based on Brownian motion and the existing wavelet methods for lead-lag analysis based on discrete-time models and enables us to analyze the multi-scale structure …
Novel framework detects lead-lag relationships in Chinese A-share market.
problem Detecting lead-lag relationships in the Chinese A-share market.
method Two-stage framework: long-term coupling via correlation, dynamic time warping, and rank-based metrics; high-frequency data analysis via cross-correlation, Granger causality, and regression models.
result Strongly coupled stock pairs often exhibit lead-lag effects, especially at finer time scales.
Abstract: A new approach to technical indicators without lag.
problem Defining classical technical indicators as bounded operators for lag-free trading.
method Using linear algebra to redefine technical indicators as bounded operators in l∞(N) space. result Demonstrated the no-lag versions of technical indicators are simpler and more effective.
New method for estimating lead-lag times between non-synchronously observed point processes.
problem Estimating lead-lag relationships between non-synchronously observed point processes.
method Formulate lead-lag estimation as CPCF shape estimation; propose kernel density estimation-based lead-lag time estimator.
result Proposed method delivers superior numerical performance and effective lead-lag time estimation.
New technique identifies lead-lag relationships in FX market during pandemic.
problem Identifying lead-lag relationships in financial markets, especially during crises.
method Dynamic Programming technique for finding optimal lead-lag path, using a loose metric.
result The proposed technique gives the best results in identifying statistically significant paths and closest forecasts.
This study examines lead-lag relationships in Chinese futures markets using high-frequency data.
problem Understanding high-frequency trading dynamics and information flow in futures markets.
method High-frequency tick-by-tick data analysis of lead-lag relationships between different maturity futures contracts.
result The near-month futures lead longer-dated contracts by one tick, with a negative feedback effect on the leading asset.
The Vector AutoRegressive (VAR) model is fundamental to the study of multivariate time series. Although VAR models are intensively investigated by many researchers, practitioners often show more interest in analyzing VARX models that incorporate the impact of unmodeled exogenous variables (X) into the VAR. However, sin…
Clusters asset classes to identify lead-lag relationships in market regimes.
problem Understanding lead-lag relationships between different asset classes.
method Defining macroeconomic regimes by clustering indices and investigating lead-lag relationships.
result Unravels market features and highlights informative market trends or risks.
Lead-lag relationships among assets represent a useful tool for analyzing high frequency financial data. However, research on these relationships predominantly focuses on correlation analyses for the dynamics of stock prices, spots and futures on market indexes, whereas foreign exchange data have been less explored. To…
We present the symmetric thermal optimal path (TOPS) method to determine the time-dependent lead-lag relationship between two stochastic time series. This novel version of the previously introduced TOP method alleviates some inconsistencies by imposing that the lead-lag relationship should be invariant with respect to …
This paper compares ML models for predicting COVID-19 trends.
problem Forecasting the spread of COVID-19 to reduce its impact.
method Applied ARDL method to identify relationships, then used ML models (SVM, RF, KNN, ANN) for prediction.
result Models accurately forecasted COVID-19 cases with low MAPE.
We study the probability distribution of stock returns at mesoscopic time lags (return horizons) ranging from about an hour to about a month. While at shorter microscopic time lags the distribution has power-law tails, for mesoscopic times the bulk of the distribution (more than 99% of the probability) follows an expon…
We employ the thermal optimal path method to explore both the long-term and short-term interaction patterns between the onshore CNY and offshore CNH exchange rates (2012-2015). For the daily data, the CNY and CNH exchange rates show a weak alternate lead-lag structure in most of the time periods. When CNY and CNH displ…
New method for identifying causal relationships in financial time series data.
problem Identifying causal relationships in nonstationary financial time series data.
method Refined constraint-based causal discovery algorithm (CD-NOTS) for nonstationary time series data.
result CD-NOTS effectively identifies causal connections in financial applications.
Financial markets worldwide do not have the same working hours. As a consequence, the study of correlation or causality between financial market indices becomes dependent on wether we should consider in computations of correlation matrices all indices in the same day or lagged indices. The answer this article proposes …
In our previous study we have presented an approach to studying lead--lag effect in financial markets using information and network theories. Methodology presented there, as well as previous studies using Pearson's correlation for the same purpose, approached the concept of lead--lag effect in a naive way. In this pape…
Methodology to measure lag relevance in time series models.
problem Measuring lag relevance in machine learning models for univariate time series.
method Ghost variables, Shapley values, additive importance measures, auto-relevance and partial auto-relevance functions, one-step forecast.
result Calculated relevance measures successfully demonstrate expected lag structure in almost all cases.
Modeling lead-lag relationship between two text corpora for improved topic modeling.
problem Recognizing the relationship between multiple text corpora for better topic modeling.
method Proposed a jointly dynamic topic model and embedding extension for large-scale text corpus.
result The proposed model can well recognize the lead-lag relationship between two text corpora and improve topic learning.
Develops variable-lag Granger causality and Transfer Entropy for time series analysis.
problem Fixed time delay assumption in Granger causality and Transfer Entropy does not hold in many applications.
method Variable-lag Granger causality and Transfer Entropy, using optimal warping path of Dynamic Time Warping (DTW).
result Proposed methods perform better than existing methods in both simulated and real-world datasets.
Often the analysis of time-dependent chemical and biophysical systems produces high-dimensional time-series data for which it can be difficult to interpret which individual features are most salient. While recent work from our group and others has demonstrated the utility of time-lagged co-variate models to study such …
Lead/lag relationships are an important stylized fact at high frequency. Some assets follow the path of others with a small time lag. We provide indicators to measure this phenomenon using tick-by-tick data. Strongly asymmetric cross-correlation functions are empirically observed, especially in the future/stock case. W…
New method speeds up lead-lag detection between asynchronous time series.
problem Slow inference of lead-lag networks between long time series.
method Derive asymptotic distribution of Transfer Entropy and introduce time-shifted time series.
result Statistically validated lead-lag networks between time series.
Recently the interest of researchers has shifted from the analysis of synchronous relationships of financial instruments to the analysis of more meaningful asynchronous relationships. Both of those analyses are concentrated only on Pearson's correlation coefficient and thus intraday lead-lag relationships associated wi…