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.
Enhances time series comparison by simplifying warping paths.
This paper describes a time-series-based classification approach to identify similarities between bio-medical-based situations. The proposed approach allows classifying collections of time-series representing bio-medical measurements, i.e., situations, regardless of the type, the length and the quantity of the time-ser…
The availability of large amounts of time series data, paired with the performance of deep-learning algorithms on a broad class of problems, has recently led to significant interest in the use of sequence-to-sequence models for time series forecasting. We provide the first theoretical analysis of this time series forec…
Evaluates six ETSC algorithms on various datasets.
Recent anomaly detection benchmarks are flawed, potentially misleading progress.
PSEUDo learns patterns in multivariate time series with locality-sensitive hashing and relevance feedback.
New TSER algorithms outperform existing methods in time series extrinsic regression.
The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks. The evaluation of these new methods requires either collecting or simulating a diverse set of time series benchmarking data to enable reliable compar…
Time series constitute a challenging data type for machine learning algorithms, due to their highly variable lengths and sparse labeling in practice. In this paper, we tackle this challenge by proposing an unsupervised method to learn universal embeddings of time series. Unlike previous works, it is scalable with respe…
We performed an empirical comparison of ICA and PCA algorithms by applying them on two simulated noisy time series with varying distribution parameters and level of noise. In general, ICA shows better results than PCA because it takes into account higher moments of data distribution. On the other hand, PCA remains quit…
New framework for analyzing hydroclimatic time series across multiple scales.
This paper presents a novel time series clustering method, the self-organising eigenspace map (SOEM), based on a generalisation of the well-known self-organising feature map (SOFM). The SOEM operates on the eigenspaces of the embedded covariance structures of time series which are related directly to modes in those tim…
This paper introduces a novel model-based clustering approach for clustering time series which present changes in regime. It consists of a mixture of polynomial regressions governed by hidden Markov chains. The underlying hidden process for each cluster activates successively several polynomial regimes during time. The…
We propose an experimental comparison between Deep Echo State Networks (DeepESNs) and gated Recurrent Neural Networks (RNNs) on multivariate time-series prediction tasks. In particular, we compare reservoir and fully-trained RNNs able to represent signals featured by multiple time-scales dynamics. The analysis is perfo…
Multi-step ahead forecasting is still an open challenge in time series forecasting. Several approaches that deal with this complex problem have been proposed in the literature but an extensive comparison on a large number of tasks is still missing. This paper aims to fill this gap by reviewing existing strategies for m…
Statistical models outperform mechanistic models in short-term COVID-19 incidence forecasts.
Paper introduces a new method for classifying interval-valued time series.
Precise financial series predicting has long been a difficult problem because of unstableness and many noises within the series. Although Traditional time series models like ARIMA and GARCH have been researched and proved to be effective in predicting, their performances are still far from satisfying. Machine Learning,…
It is very vital for suppliers and distributors to predict the deregulated electricity prices for creating their bidding strategies in the competitive market area. Pre requirement of succeeding in this field, accurate and suitable electricity tariff price forecasting tools are needed. In the presence of effective forec…
A new differentiable divergence for time series comparison.
MD-CGAN models forecast time series with probabilistic posterior distributions.
Study evaluates local explanation methods for time series forecasting.
ABBA creates a new symbolic time series representation based on Brownian bridge.
Deep learning transforms time series into images for anomaly detection in industrial assets.
Topological attention improves forecasting of univariate time series.
Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through s…
Paper uses LLMs for financial forecasting, overcoming sequence reasoning and multi-modal challenges.
Deep learning has achieved impressive prediction performance in the field of sequence learning recently. Dissolved oxygen prediction, as a kind of time-series forecasting, is suitable for this technique. Although many researchers have developed hybrid models or variant models based on deep learning techniques, there is…
MegazordNet combines stats and ML for better financial time series forecasting.
Simple mean and std-based classifier outperforms chance on 69 out of 128 time-series problems.
Combines CNN and Transformer for financial time series forecasting.
We review statistical properties of models generated by the application of a (positive and negative order) fractional derivative operator to a standard random walk and show that the resulting stochastic walks display slowly-decaying autocorrelation functions. The relation between these correlated walks and the well-kno…
T2IVAE detects anomalies in time series data with high accuracy.
Proposes neural SDEs with change points for better time series modeling.
Translating potential disease biomarkers between multi-species 'omics' experiments is a new direction in biomedical research. The existing methods are limited to simple experimental setups such as basic healthy-diseased comparisons. Most of these methods also require an a priori matching of the variables (e.g., genes o…
Proposes a neural network for handling multi-sensor time series with varying input dimensions.
This work compares OmniAnomaly with PCA for MTSAD, finding PCA can match or outperform OmniAnomaly.
Inspired by trading, this method segments time series efficiently.
New algorithms predict causal links better than traditional methods in time series data.
ElasTST improves time-series forecasting across varying horizons.
This study compares deep generative models to traditional methods for generating financial time series.
Delay-SDE-net models time series with memory and uncertainty, outperforming other models.
Recognizing subtle historical patterns is central to modeling and forecasting problems in time series analysis. Here we introduce and develop a new approach to quantify deviations in the underlying hidden generators of observed data streams, resulting in a new efficiently computable universal metric for time series. Th…
A method to improve time series forecasting by dynamically adjusting weights of forecasters.
Studying the impact of climate change on precipitation is constrained by finding a way to evaluate the evolution of precipitation variability over time. Classical approaches (feature-based) have shown their limitations for this issue due to the intermittent and irregular nature of precipitation. In this study, we prese…
For time series comparisons, it has often been observed that z-score normalized Euclidean distances far outperform the unnormalized variant. In this paper we show that a z-score normalized, squared Euclidean Distance is, in fact, equal to a distance based on Pearson Correlation. This has profound impact on many distanc…
NAS for financial time series forecasts using chain-structured architectures.
Generative model for time series using Schrödinger bridges with jumps.