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.
Novel algorithm SAODE improves high-dimensional stream classification in seasonal data.
Proposes a method for forecasting time series with multiple seasonality.
Study improves seasonal forecasts using deep learning.
Multiple seasonal patterns play a key role in time series forecasting, especially for business time series where seasonal effects are often dramatic. Previous approaches including Fourier decomposition, exponential smoothing, and seasonal autoregressive integrated moving average (SARIMA) models do not reflect the disti…
This paper examines the intra-day seasonality of transacted limit and market orders in the DEM/USD foreign exchange market. Empirical analysis of completed transactions data based on the Dealing 2000-2 electronic inter-dealer broking system indicates significant evidence of intraday seasonality in returns and return vo…
We introduce a multi-factor stochastic volatility model for commodities that incorporates seasonality and the Samuelson effect. Conditions on the seasonal term under which the corresponding volatility factor is well-defined are given, and five different specifications of the seasonality pattern are proposed. We calcula…
We introduce a multi-factor stochastic volatility model based on the CIR/Heston volatility process that incorporates seasonality and the Samuelson effect. First, we give conditions on the seasonal term under which the corresponding volatility factor is well-defined. These conditions appear to be rather mild. Second, we…
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…
New model detects anomalies robustly in noisy, seasonal multivariate time series.
Modeling daily river flow distribution with seasonal and long-term trends.
Generating forecasts for time series with multiple seasonal cycles is an important use-case for many industries nowadays. Accounting for the multi-seasonal patterns becomes necessary to generate more accurate and meaningful forecasts in these contexts. In this paper, we propose Long Short-Term Memory Multi-Seasonal Net…
ST-MTM models complex time series by decomposing and masking seasonal and trend components.
Deep learning models perform variably across continents/seasons in land cover mapping.
Study analyzes seasonal hydroclimatic features across climates and continents.
Study combines variational inference and transformers for seasonal climate predictions.
Machine learning predicts seasonal precipitation for East Africa.
KEDformer improves long-term time series forecasting with seasonal-trend decomposition.
Robust PCA detects anomalies and fills gaps in seasonal time series data.
Accurate and reliable predictions of infectious disease dynamics can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task, using different model structures, covariates, and targets for prediction. Expe…
Advanced forecasting models outperform Holt-Winters and ARIMA for stock market data.
The in-depth analysis of time series has gained a lot of research interest in recent years, with the identification of periodic patterns being one important aspect. Many of the methods for identifying periodic patterns require time series' season length as input parameter. There exist only a few algorithms for automati…
Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decom…
Recent advances in the understanding of time series permit to clarify seasonalities and cycles, which might be rather obscure in today's literature. A theorem due to P. Cartier and Y. Perrin, which was published only recently, in 1995, and several time scales yield, perhaps for the first time, a clear-cut definition of…
EVARS-GPR refines Gaussian Process Regression for seasonal data with sudden scale changes.
Novel approach predicts long-term seasonal component of electricity prices for improved forecasting.
Gold prices show seasonal behavior, with January and July having opposite returns.
Enhanced time series forecasting with improved trend and seasonal components.
Improved electrical load forecasting model using Fourier-enhanced RNN.
New method corrects seasonal Arctic sea ice predictions with probabilistic models.
Seasonal influenza infects between 10 and 50 million people in the United States every year, overburdening hospitals during weeks of peak incidence. Named by the CDC as an important tool to fight the damaging effects of these epidemics, accurate forecasts of influenza and influenza-like illness (ILI) forewarn public he…
Machine learning improves sub-seasonal climate forecasting, especially gradient boosting and deep learning.
Robust anomaly detection is a requirement for monitoring complex modern systems with applications such as cyber-security, fraud prevention, and maintenance. These systems generate multiple correlated time series that are highly seasonal and noisy. This paper presents a novel unsupervised deep learning architecture for …
Framework isolates causal effects from time series data, improving accuracy under non-stationarity and autocorrelation.
Deep learning models predict call center volumes with seasonal patterns.
A novel algorithm predicts customized allergy seasons using multi-variate triple-regression.
In this paper we perform a statistical analysis over the returns and relative prices of the CAC and the S\&P with the purpose of analyzing the intra-day seasonalities of single and cross-sectional stock dynamics. In order to do that, we characterized the dynamics of a stock (or a set of stocks) by the evolut…
We present a simple algorithm to forecast vector time series, that is robust against missing data, in both training and inference. It models seasonal annual, weekly, and daily baselines, and a Gaussian process for the seasonally-adjusted residuals. We develop a custom truncated eigendecomposition to fit a low-rank plus…
Providing long-range forecasts is a fundamental challenge in time series modeling, which is only compounded by the challenge of having to form such forecasts when a time series has never previously been observed. The latter challenge is the time series version of the cold-start problem seen in recommender systems which…
The paper identifies key macroeconomic events affecting exchange rate volatility.
Enhances neural networks with prior knowledge through a composite kernel.
CSP improves time-series forecasting without training, outperforming DeepNPTS in speed and accuracy.
NoTMF forecasts sparse urban road movement speeds with nonstationary temporal matrix factorization.
New bandit algorithm detects and adapts to seasonal changes in rewards.
Develops a framework to estimate NBA player salary ROI.
Study improves PM concentration forecasting using MCCR loss.
In this paper, we employ machine learning techniques to analyze seventeen seasons (1999-2000 to 2015-2016) of NBA regular season data from every team to determine the common characteristics among NBA playoff teams. Each team was characterized by 26 predictor variables and one binary response variable taking on a value …
Time-series forecasting is an important task in both academic and industry, which can be applied to solve many real forecasting problems like stock, water-supply, and sales predictions. In this paper, we study the case of retailers' sales forecasting on Tmall|the world's leading online B2C platform. By analyzing the da…
Graph neural networks improve El Niño forecasts.