The paper explores efficient global optimization using Kriging models.
problem Optimizing complex functions with limited evaluations.
method Implemented UK-EGO framework with automatic trend function selection.
result PCK-EGO variant showed the best performance.
In this paper we study automatically recognized trends and investigate their statistics. To do that we introduce the notion of a wavelength for time series via cross correlation and use this wavelength to calibrate the 1-2-3 trend indicator of Maier-Paape [Automatic One Two Three, Quantitative Finance, 2013] to automat…
Tangent Works won GEFCom 2017 using automatic model building.
problem Forecasting time series with historical temperature shuffling.
method Automatic model building using Tangent Information Modeller (TIM) with historical temperature shuffling and decision on trend variable.
result Automated model building setup won the competition.
This paper presents the beginnings of an automatic statistician, focusing on regression problems. Our system explores an open-ended space of statistical models to discover a good explanation of a data set, and then produces a detailed report with figures and natural-language text. Our approach treats unknown regression…
DoubleAdapt improves stock trend forecasting by adapting models to evolving data.
problem Incremental learning for stock trend forecasting is challenging due to distribution shifts.
method DoubleAdapt framework with two adapters for data and model adaptation.
result DoubleAdapt achieves state-of-the-art predictive performance on real-world stock datasets.
Automatically computes reference ranges for UK Biobank cardiac data.
problem Improving healthcare by discovering patterns in large-scale population data.
method Fully automatic pipeline for 3D cardiac MR image analysis.
result Statistically significant agreement between manual and automatic indexes.
GP model for time series forecasting with priors.
problem Automatic selection of optimal kernels and reliable estimation of hyperparameters.
method Fixed composition of kernels, automatic relevance determination (ARD), empirical Bayes priors.
result GP model is more accurate than state-of-the-art models.
FFRK automatically extracts features for spatial interpolation without external variables.
problem Spatial interpolation challenges, especially nonstationarity and lack of explanatory variables.
method Feature-Free Regression Kriging (FFRK) method that extracts geospatial features.
result FFRK outperforms classical methods in predicting heavy metal concentrations.
Deep Reinforcement Learning (DRL) is a trending field of research, showing great promise in many challenging problems such as playing Atari, solving Go and controlling robots. While DRL agents perform well in practice we are still missing the tools to analayze their performance and visualize the temporal abstractions t…
Model captures sleep patterns in infant data.
problem Infant sleep data analysis with missing values.
method Nonparametric model using low-rank matrix factorization with time-smoothing regularization.
result Extracts meaningful sleep patterns and trends.
Crowdbreaks tracks health trends using social media and crowdsourcing.
problem Challenges in tracking health trends using social media data.
method Open platform for continuous crowdsourced labelling of social media content.
result Accelerates research process in public health.
The detrending moving average (DMA) algorithm is one of the best performing methods to quantify the long-term correlations in nonstationary time series. Many long-term correlated time series in real systems contain various trends. We investigate the effects of polynomial trends on the scaling behaviors and the performa…
Convolutional Neural Networks predict forex trends from charts.
problem Predicting forex trends from trading charts.
method Pre-process data, train CNN, evaluate model performance.
result Trades strategies can be automatically generated.
We study trend filtering, a recently proposed tool of Kim et al. [SIAM Rev. 51 (2009) 339-360] for nonparametric regression. The trend filtering estimate is defined as the minimizer of a penalized least squares criterion, in which the penalty term sums the absolute kth order discrete derivatives over the input points…
Kronecker trend filtering improves lattice data smoothing.
problem Estimating smooth functions on lattice data.
method Penalized least squares with Kronecker products of univariate trend filtering penalties.
result Kronecker trend filtering outperforms linear smoothers in high dimensions.
Charts are an excellent way to convey patterns and trends in data, but they do not facilitate further modeling of the data or close inspection of individual data points. We present a fully automated system for extracting the numerical values of data points from images of scatter plots. We use deep learning techniques t…
This paper presents a fast and robust algorithm for trend filtering, a recently developed nonparametric regression tool. It has been shown that, for estimating functions whose derivatives are of bounded variation, trend filtering achieves the minimax optimal error rate, while other popular methods like smoothing spline…
We investigated distributions of short term price trends for high frequency stock market data. A number of trends as a function of their lengths was measured. We found that such a distribution does not fit to results following from an uncorrelated stochastic process. We proposed a simple model with a memory that gives …
The study revises GDPpc trends and redistributes economic power among countries.
problem Analyzing global economic power shifts over time.
method Modeling GDPpc as a trend and fluctuations, analyzing historical data.
result Revised GDPpc trends show significant shifts in global economic power.
A method decomposes battery cell capacity trends using MCGP for high accuracy and uncertainty.
problem Forecasting lithium-ion battery cells capacity with high accuracy and uncertainty.
method Multi-Output Convolved Gaussian Process (MCGP) for latent function decomposition.
result The MCGP method provides high prediction accuracy and uncertainty information.
We study additive models built with trend filtering, i.e., additive models whose components are each regularized by the (discrete) total variation of their kth (discrete) derivative, for a chosen integer k≥0. This results in kth degree piecewise polynomial components, (e.g., k=0 gives piecewise constant co…
GShard enables scaling of large neural networks with automatic sharding and lightweight APIs.
problem Scaling neural networks to handle vast training data and compute efficiently.
method GShard uses lightweight annotation APIs and XLA compiler extensions for parallel computation.
result GShard successfully trained a 600 billion parameter model on 2048 TPUs in 4 days.
Linear trends in classifier accuracy observed under distribution shift.
problem Understanding why classifier accuracies show linear trends under distribution shift.
method Assumed model similarity and verified empirically.
result Linear trend in classifier accuracy occurs unless distribution shift is large.
A new standpoint on financial time series, without the use of any mathematical model and of probabilistic tools, yields not only a rigorous approach of trends and volatility, but also efficient calculations which were already successfully applied in automatic control and in signal processing. It is based on a theorem d…
Growth rate of real GDP per capita is represented as a sum of two components -- a monotonically decreasing economic trend and fluctuations related to a specific age population change. The economic trend is modeled by an inverse function of real GDP per capita with a numerator potentially constant for the largest develo…
We examine several recently suggested methods for the detection of long-range correlations in data series based on similar ideas as the well-established Detrended Fluctuation Analysis (DFA). In particular, we present a detailed comparison between the regular DFA and two recently suggested methods: the Centered Moving A…
System for automatic differentiation in a functional array-processing language.
problem Efficient automatic differentiation in functional array languages.
method Automatic differentiation in a higher-order functional array-processing language with source-to-source support and global optimizations.
result The system outperforms state-of-the-art tools on machine learning and computer vision benchmarks.
Boosting improves trend detection in financial data.
problem Discovering trends in financial data during crises and recoveries.
method Extends boosting to higher order integrated processes and series with roots near unity.
result Boosting captures downturns and recoveries more accurately.
Paper tackles optimal redemption of stock loans under uncertain stock trends.
problem Optimal redemption strategy of stock loans under drift uncertainty.
method Probabilistic and functional analysis to handle degenerate parabolic HJB equation.
result Optimal redemption strategies differ significantly between bull and bear trends.
In this paper, we consider a stochastic asset price model where the trend is an unobservable Ornstein Uhlenbeck process. We first review some classical results from Kalman filtering. Expectedly, the choice of the parameters is crucial to put it into practice. For this purpose, we obtain the likelihood in closed form, a…
Combines kernels to create flexible priors in BNNs for seasonal and trend data.
problem Creating flexible priors in Bayesian neural networks for complex data.
method Derives BNN architectures from kernel combinations and periodic functions.
result BNNs can produce periodic kernels useful for capturing seasonal and trend data.
Martingale Doppelgänger-Eval benchmarks VLMs on candlestick evidence vs. trend extrapolation
problem Auditing whether VLMs use chart evidence or trend extrapolation
method Proving formal limitations and designing controlled mechanisms
result Identifying regression coefficients for evidence vs. trend
Introduces a new theoretical framework for exponential smoothing.
problem Theoretical foundation and robustness of simple exponential smoothing.
method Stochastic gradient ascent to optimize Gaussian log-likelihood functions.
result Simple exponential smoothing converges to the trend of a trend-stationary process.
A new estimator reduces bias and improves efficiency for staggered adoption studies.
problem Bias in difference-in-differences estimates for staggered adoption studies.
method Fused Extended Two-Way Fixed Effects (FETWFE) estimator with automatic parameter selection.
result FETWFE identifies correct restrictions with probability tending to one, improving efficiency.
Develops a new trend power indicator using DSP techniques.
problem Determining the strength and reversibility of trends.
method Derives a novel indicator using digital signal processing.
result Accuracy of the new indicator correlates with PNL performance.
Optimal binning method for numeric targets using mathematical programming.
problem Optimizing the discretization of numeric variables for classification.
method Mathematical programming formulation for binary, continuous, and multi-class targets with constraints.
result Convex mixed-integer programming formulations for all target types.
Study uses Hawkes processes to analyze stock market contagion in China.
problem Understanding contagion in Chinese stock market.
method Fitting Hawkes processes to daily returns and sector indices.
result Identifies long-term dependencies and trending patterns in sector indices.
Deep learning improves human affect recognition in natural settings.
problem Automatic recognition of human emotions in real-world scenarios.
method Review and analysis of 233 studies using deep neural networks.
result Deep learning significantly enhances affect recognition from multimodal sensor data.
Forecast future volatilities and correlations based on current trends.
problem Predict future volatilities and correlations in financial markets.
method Use cubic and quadratic polynomials of current trend strengths.
result Accurate quantification of trend effects on volatilities and correlations.
Explains the difference between EMA and moving EMA, focusing on market trend indicators.
problem Understanding the difference between exponential moving average and moving exponential average.
method Explains the mathematical tools and definitions of trend indicators.
result Discusses the properties of the MACD indicator and its use in market trend analysis.
Bitcoin's attention is linked to Google Trends data, not general uncertainty.
problem Bitcoin's correlation with Google Trends data was previously misunderstood.
method Analyzed bidirectional relationships between Bitcoin returns and Google Trends attention over six days.
result Information flows from Bitcoin volatility to Google Trends attention, not the other way.
RobustTrend filters time series trends robustly against outliers and abrupt changes.
problem Extracting accurate trend signals from noisy, potentially abrupt-changing time series.
method Uses Huber loss for outlier suppression and a combination of first and second order differences for regularization.
result Our algorithm outperforms existing methods in synthetic and real-world datasets.
LARA forecasts financial asset trends by refining noisy labels and extracting profitable samples.
problem Low signal-to-noise ratio and stochastic nature of financial data lead to poor predictions.
method LARA combines LA-Attention and RA-Labeling to refine and extract profitable samples.
result LARA significantly outperforms existing methods on Qlib platform.
Study shows RNNs are effective for trend detection in time series.
problem Detecting trends in noisy time series data.
method Empirical investigation of standard RNNs for trend detection using simulated data.
result Standard RNNs structures outperform other estimators in trend detection.
Enhanced trend-following strategy using network momentum for commodity futures.
problem Improving systematic trend-following in commodity futures markets.
method Combines univariate and cross-sectional trend indicators, including network momentum.
result Statistically significant improvements in portfolio performance metrics.
This paper proposes automatic tuning of Bayesian Optimization's acquisition function.
problem Optimizing black-box functions with noisy, expensive evaluations and hyperparameter tuning.
method Exploring heuristics to automatically tune acquisition functions in Bayesian Optimization.
result Demonstrates effectiveness of heuristics in automatic Bayesian Optimization.
BASS efficiently learns time-varying graphs with low complexity and automatic tuning.
problem Estimating time-varying graphical models with efficient and automatic parameter tuning.
method BASS uses temporally-dependent spike-and-slab priors and variational inference to learn graph structures efficiently.
result BASS outperforms existing methods in recovering true graphs, especially for high-dimensional cases.
KINN integrates expert knowledge into neural networks to improve performance.
problem Lack of expert knowledge in neural networks, especially in time-series domains.
method Integrates expert knowledge through a residual knowledge incorporation scheme.
result Significantly improved performance on real-world traffic flow prediction.