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.
KEDformer improves long-term time series forecasting with seasonal-trend decomposition.
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…
This paper attempts to provide a state of the art in trend prediction using news headlines. We present the research done on predicting DJIA trends using Natural Language Processing. We will explain the different algorithms we have used as well as the various embedding techniques attempted. We rely on statistical and de…
This paper proposes a framework to predict long-term trends and short-term fluctuations in multivariate time series.
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…
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.
FFRK automatically extracts features for spatial interpolation without external variables.
HybridCGAN improves portfolio analysis by balancing trend prediction and market uncertainty.
This research examines how data transformations affect adversarial robustness in recurrent neural networks.
A new framework forecasts stock trends by mining shared information from concepts.
A new SOHP filter improves trend estimation in economic time series.
Study uncovers financial trends from cross-lingual news data.
REST framework predicts stock trends by considering stock-specific and related-stock events.
Multivariate time series are routinely encountered in real-world applications, and in many cases, these time series are strongly correlated. In this paper, we present a deep learning structural time series model which can (i) handle correlated multivariate time series input, and (ii) forecast the targeted temporal sequ…
ACGAN improves portfolio allocation by learning trends and uncertainty.
The paper analyzes CEX-DEX arbitrage and profitability on Ethereum, revealing centralization trends and market impacts.
We study the nature of fluctuations in variety of price indices involving companies listed on the New York Stock Exchange. The fluctuations at multiple scales are extracted through the use of wavelets belonging to Daubechies basis. The fact that these basis sets satisfy vanishing moments conditions makes them ideal to …
In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which med…
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…
With the popularity of social networks, and e-commerce websites, sentiment analysis has become a more active area of research in the past few years. On a high level, sentiment analysis tries to understand the public opinion about a specific product or topic, or trends from reviews or tweets. Sentiment analysis plays an…
The paper uses 3D shapes to reveal sundial design adjustments based on latitude.
Enhanced time series forecasting with improved trend and seasonal components.
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…
Enhances time-series regression trees with latent factors for robust financial analysis.
LARA forecasts financial asset trends by refining noisy labels and extracting profitable samples.
The large volume of text in electronic healthcare records often remains underused due to a lack of methodologies to extract interpretable content. Here we present an unsupervised framework for the analysis of free text that combines text-embedding with paragraph vectors and graph-theoretical multiscale community detect…
A text mining approach is proposed based on latent Dirichlet allocation (LDA) to analyze the Consumer Financial Protection Bureau (CFPB) consumer complaints. The proposed approach aims to extract latent topics in the CFPB complaint narratives, and explores their associated trends over time. The time trends will then be…
New framework predicts cryptocurrency trends by analyzing news and market data.
A novel approach predicts long-term stock price trends using 2D-convolutional encoders and semantic segmentation.
FinDKG uses LLMs to detect financial trends from news articles.
Feature Learning aims to extract relevant information contained in data sets in an automated fashion. It is driving force behind the current deep learning trend, a set of methods that have had widespread empirical success. What is lacking is a theoretical understanding of different feature learning schemes. This work p…
This study uses AI to analyze financial market coverage from YouTube videos.
GraphCNNpred predicts stock market indices using deep learning.
DGDS uses documents to center conversations, promising broader AI understanding.
Hybrid QNN-LSTM predicts financial stock market trends using quantum computing.
Dynamic factor analysis reveals insights into Philippine stock market dynamics.
Recent works have shown that social media platforms are able to influence the trends of stock price movements. However, existing works have majorly focused on the U.S. stock market and lacked attention to certain emerging countries such as China, where retail investors dominate the market. In this regard, as retail inv…
Recent trends focusing on Industry 4.0 concept and smart manufacturing arise a data-driven fault diagnosis as key topic in condition-based maintenance. Fault diagnosis is considered as an essential task in rotary machinery since possibility of an early detection and diagnosis of the faulty condition can save both time …
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…
Emotion recognition based on EEG has become an active research area. As one of the machine learning models, CNN has been utilized to solve diverse problems including issues in this domain. In this work, a study of CNN and its spatiotemporal feature extraction has been conducted in order to explore capabilities of the m…
Financial data has been extensively studied for correlations using Pearson's cross-correlation coefficient ρ as the point of departure. We employ an estimator based on recurrence plots --- the Correlation of Probability of Recurrence (CPR) --- to analyze connections between nine stock indices spread worldwide. We sugge…
PerPCA separates unique and shared features from heterogeneous data.
A novel framework extracts essential factors from order flow data for high-frequency trading.
Several studies have established the predictive power of the yield curve in terms of real economic activity. In this paper we use data for a variety of E.U. countries: both EMU (Germany, France, Italy) and non-EMU members (Sweden and the U.K.). The data used range from 1991:Q1 to 2009:Q1. For each country, we extract t…
Develops a new trend power indicator using DSP techniques.
SPREV simplifies visualization of complex labeled datasets.
Forecast future volatilities and correlations based on current trends.