Detecting structure in noisy time series is a difficult task. One intuitive feature is the notion of trend. From theoretical hints and using simulated time series, we empirically investigate the efficiency of standard recurrent neural networks (RNNs) to detect trends. We show the overall superiority and versatility of …
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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,695 papers · 148 categories
Trend · papers per month
48 results for “trend detection”
In this paper we study the estimation of changing trends in time-series using trend filtering. This method generalizes 1D Total Variation (TV) denoising for detection of step changes in means to detecting changes in trends, and it relies on a convex optimization problem for which there are very efficient numer…
Much information available on the web is copied, reused or rephrased. The phenomenon that multiple web sources pick up certain information is often called trend. A central problem in the context of web data mining is to detect those web sources that are first to publish information which will give rise to a trend. We p…
Study detects emerging trends in financial news articles about Microsoft.
problem Challenges in identifying trends in long-form financial news articles.
method Topic modeling and term frequency for keyword similarity analysis.
result Demonstrates the influence of the pandemic on Microsoft.
A new framework detects changepoints in complex data.
problem Detecting structural changes in data with various patterns and trends.
method Iteratively Reweighted Fused Lasso (IRFL) for L0 model selection.
result IRFL achieves accurate changepoint detection across various challenging scenarios.
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.
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.
Deep learning detects sleep state fluctuations in neonates from single EEG channel.
problem Monitoring sleep state fluctuations in neonatal intensive care units.
method Deep learning-based algorithm trained on 53 EEG recordings, validated on 30 polysomnography recordings.
result High accuracy (90%) in detecting quiet sleep states from single EEG channel, generalizing well to external dataset.
GC 2022 challenges real-time trend detection in financial tick data.
problem Efficiently detect trading trends in high-volume financial tick data.
method Real-time complex event processing of tick data, focusing on trend indicators and patterns.
result Participants must build reusable and practical solutions for real-life trading decisions.
Extracting the underlying trend signal is a crucial step to facilitate time series analysis like forecasting and anomaly detection. Besides noise signal, time series can contain not only outliers but also abrupt trend changes in real-world scenarios. To deal with these challenges, we propose a robust trend filtering al…
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.
In this article, we discuss various implementation of L1 filtering in order to detect some properties of noisy signals. This filter consists of using a L1 penalty condition in order to obtain the filtered signal composed by a set of straight trends or steps. This penalty condition, which determines the number of breaks…
BFCR detects anomalies in 1D data efficiently.
problem Anomaly detection in 1D data.
method Braced Fourier Continuation and Regression (BFCR) for efficient anomaly detection.
result BFCR trend lines effectively detect anomalies in 1D 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
Piecewise Aggregate Approximation (PAA) is a competitive basic dimension reduction method for high-dimensional time series mining. When deployed, however, the limitations are obvious that some important information will be missed, especially the trend. In this paper, we propose two new approaches for time series that u…
Sequencer algorithm detects main trends in complex datasets.
problem Detecting simple phenomena in complex data.
method Constructs graphs from data similarities, identifies elongated trends.
result Sequencer outperforms t-SNE and UMAP in identifying main trends.
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networkscs.LG
RobustTAD detects anomalies in diverse time series data.
problem Effective anomaly detection for complex time series data.
method Robust seasonal-trend decomposition + CNN architecture with data augmentation.
result RobustTAD outperforms other methods on public datasets.
FinDKG uses LLMs to detect financial trends from news articles.
problem Detecting global financial trends from unstructured text data.
method Fine-tuned LLMs for generating DKGs, KGTransformer for analysis.
result KGTransformer outperforms existing thematic ETFs in financial thematic investing.
Bayesian model predicts oncology demand trends with high accuracy.
problem Accurate forecasting of oncology demand for resource planning.
method Boosting-based Bayesian conjugate models for Poisson process.
result Model outperforms other methods in trend detection accuracy.
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…
Decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting. Although numerous methods have been proposed, there are still many time series characteristics exhibiting in real-world data which are not addressed properl…
A hybrid approach detects financial market regime switches using PCA and k-means.
problem Detecting regime switches in financial markets for trend forecasting.
method Dimensionality reduction with PCA and clustering with k-means.
result Trading strategies based on detected regimes show improved performance.
We first review empirical evidence that asset prices have had episodes of large fluctuations and been inefficient for at least 200 years. We briefly review recent theoretical results as well as the neurological basis of trend following and finally argue that these asset price properties can be attributed to two fundame…
Price dynamics is analyzed in terms of a model which includes the possibility of effective forces due to trend followers or trend adverse strategies. The method is tested on the data of a minority-majority model and indeed it is capable of reconstructing the prevailing traders' strategies in a given time interval. Then…
This work tried to detect the existence of a relationship between the graphic signals - or patterns - observed day by day in the Brazilian stock market and the trends which happen after these signals, within a period of 8 years, for a number of securities. The results obtained from this study show evidence of the exist…
Multi-Agent Analysis of Off-Exchange Public Information for Cryptocurrency Market Trend Predictionq-fin.CP
New framework predicts cryptocurrency trends by analyzing news and market data.
problem Cryptocurrency market volatility and news sensitivity challenges prediction accuracy.
method Multi-agent system with three innovations: news analysis, fusion mechanism, and coordination architecture.
result Statistically significant improvements over state-of-the-art methods.
Early detection and precise characterization of emerging topics in text streams can be highly useful in applications such as timely and targeted public health interventions and discovering evolving regional business trends. Many methods have been proposed for detecting emerging events in text streams using topic modeli…
Model predicts stock market trends for better investment decisions.
problem Identifying optimal times to buy and sell stocks.
method XGBoost machine learning model using time series data and feature engineering.
result Model accurately predicts stock market trends and their endpoints.
We introduce an algorithmic method for population anomaly detection based on gaussianization through an adversarial autoencoder. This method is applicable to detection of `soft' anomalies in arbitrarily distributed highly-dimensional data. A soft, or population, anomaly is characterized by a shift in the distribution o…
Hierarchical hidden Markov models predict market trends in financial time series.
problem Misinterpretation of short-term price fluctuations as long-term trend changes.
method Hierarchical hidden Markov models to capture both short- and long-term trends.
result Hierarchical models provide a comprehensive picture of financial markets.
The initial analysis of any large data set can be divided into two phases: (1) the identification of common trends or patterns and (2) the identification of anomalies or outliers that deviate from those trends. We focus on the goal of detecting observations with novel content, which can alert us to artifacts in the dat…
Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detectionstat.ML
Improved trading strategy using deep learning and changepoint detection for market changes.
problem Traditional momentum strategies struggle with rapid market changes, especially after trend reversals.
method Inserted an online changepoint detection module into a Deep Momentum Network (DMN) pipeline.
result Improvement in Sharpe ratio by one-third over 1995-2020 period, especially beneficial in nonstationary periods.
Survey categorizes time series anomaly detection methods.
problem Need for anomaly detection in time series data.
method Process-centric taxonomy of anomaly detection methods.
result Meta-analysis of time series anomaly detection trends.
Study analyzes European energy markets' reactions to 2022 events using Bayesian methods.
problem Detecting structural breakpoints in energy and financial markets during turbulent times.
method Combines Hurst exponent for market efficiency, BEAST for abrupt changes, and seasonal/trend analysis.
result Markets exhibit varied reactions to critical events, affecting their trends and breakpoints.
CNNs identify stock market trend endpoints based on expert opinion.
problem Finding optimal entry and exit points for stock market trends.
method Three CNN submodels sequentially identify changepoints, locate them, and classify trends as upward, downward, or flat.
result CNNs can identify long-term trends based on expert opinion, offering a new approach to stock market analysis.
Detects anomalies in product health metrics at eBay for better alerts.
problem Detecting anomalies in unsupervised product health metrics at eBay.
method Developed a Moving Metric Detector (MMD) for anomaly detection and a point-wise ranking model for alert retrieval.
result Improves alert precision and avoids alert spamming in eBay production.
Unified taxonomy categorizes DL-based MTSAD methods.
problem Lack of systematization in MTSAD research.
method Two-fold approach: derived from methodological studies and reviewed papers.
result Convergence toward Transformer-based and reconstruction/prediction models.
Since with massive data growth, the need for autonomous and generic anomaly detection system is increased. However, developing one stand-alone generic anomaly detection system that is accurate and fast is still a challenge. In this paper, we propose conventional time-series analysis approaches, the Seasonal Autoregress…
Anomaly detection for high-dimensional data using large deviations principle.
problem Challenges in anomaly detection for high-dimensional data.
method Large Deviations Anomaly Detection (LAD) algorithm.
result Outperforms state-of-the-art methods on high-dimensional data sets.
Aiming for Relevancecs.LG
Novel metrics improve machine learning models for ICU patient care.
problem Predicting vital sign trajectories for early detection of adverse events.
method Developed novel performance metrics aligned with clinical contexts, validated on simulated and real datasets, and optimized neural networks using these metrics.
result Neural networks trained with these metrics excel in predicting clinically significant events.
A new oscillator measures trending behavior of financial instruments.
problem Detecting underlying deterministic components in financial market prices.
method Financial market geometry and tube oscillator derived from past history.
result Simple trading strategy based on tube oscillator leads to consistent positive returns.
Skin cancer is a major public health problem around the world. Its early detection is very important to increase patient prognostics. However, the lack of qualified professionals and medical instruments are significant issues in this field. In this context, over the past few years, deep learning models applied to autom…
Profitable Strategy Design for Trades on Cryptocurrency Markets with Machine Learning Techniquesq-fin.TR
The study uses machine learning to predict cryptocurrency market trends and design profitable trading strategies.
problem Predicting cryptocurrency market trends for profitable trading.
method Applied k-Nearest Neighbours, eXtreme Gradient Boosting, and Random Forest classifiers to detect trends.
result High profit factor of 1.60 for unseen data, showing promising results.
This work studies the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph, where the value at each node can be vector-valued. We extend the graph trend filtering framework to denoising vector-valued graph signals with a family of non-convex regularizers, which exhibi…
Recently, advances in machine learning techniques have attracted the attention of the research community to build intrusion detection systems (IDS) that can detect anomalies in the network traffic. Most of the research works, however, do not differentiate among different types of attacks. This is, in fact, necessary fo…
Identification of physiological shock in intensive care units via Bayesian regime switching modelsstat.AP
Bayesian model detects internal bleeding in ICU patients.
problem Late detection of internal bleeding in ICU patients.
method Bayesian regime switching model analyzing vitals and labs.
result Probabilistic assessment of patient's physiological state.
This paper models stock prices using a Janardan Galton Watson process.
problem Modeling stock price fluctuations and predicting market trends.
method Extends Janardan Galton Watson process to model stock prices, considering initial close price and number of offspring.
result The model predicts return values and probability of market extinction.
We propose a nonparametric model for time series with missing data based on low-rank matrix factorization. The model expresses each instance in a set of time series as a linear combination of a small number of shared basis functions. Constraining the functions and the corresponding coefficients to be nonnegative yields…