Enhances time series comparison by simplifying warping paths.
problem Lack of qualitative comparison on top of elastic distance measures.
method Proposes a technique to simplify warping paths for better visualization and interpretation.
result Clearer representation of how subsequences match between time series.
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.
problem Comparing ETSC algorithms for different data characteristics.
method Empirical comparison of six ETSC algorithms on public and new datasets.
result Provides insights into algorithm performance on real-life applications.
Recent anomaly detection benchmarks are flawed, potentially misleading progress.
problem Flawed benchmark datasets create misleading progress reports.
method Identified four flaws in benchmark datasets and introduced a new archive.
result Published comparisons may be unreliable due to flaws in benchmark datasets.
PSEUDo learns patterns in multivariate time series with locality-sensitive hashing and relevance feedback.
problem Efficient pattern detection in large, multi-track sequential data with high variance and lack of ground truth.
method Query-aware locality-sensitive hashing for feature learning, sub-linear training and inference time.
result PSEUDo achieves sub-linear time efficiency for pattern modeling and comparison of 10,000 multivariate time series.
New TSER algorithms outperform existing methods in time series extrinsic regression.
problem Improving time series extrinsic regression models.
method Extended TSER archive, introduced two new algorithms (FreshPRINCE and DrCIF), compared with rotation forest.
result DrCIF and FreshPRINCE models significantly outperform existing methods.
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.
problem Understanding geophysical processes and evaluating stochastic models across different time scales.
method A novel feature compilation method for multi-scale hydroclimatic analyses.
result Identified similarities and differences in time series types across various temporal resolutions.
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.
problem Comparing accuracy of mechanistic vs statistical models for short-term COVID-19 incidence forecasts.
method Empirical comparison of forecasts from mechanistic and statistical models using daily incidence data from six US states.
result Statistical models are at least as accurate as mechanistic models and better capture volatility.
Paper introduces a new method for classifying interval-valued time series.
problem Classification of interval-valued time series.
method Extends point-valued time series imaging methods to interval-valued scenarios using DK-distance and employs deep learning for classification. result Proposed method achieves superior classification performance compared to existing methods.
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.
problem Computing discrepancies between time series of varying lengths.
method Proposed a new divergence, soft-DTW divergence, addressing issues of differentiability and positivity.
result Showed that the new divergence is a valid divergence: non-negative and minimized when time series are equal.
MD-CGAN models forecast time series with probabilistic posterior distributions.
problem Limited applications of GANs in time series forecasting, especially with probabilistic predictions.
method Mixture Density Conditional Generative Adversarial Model (MD-CGAN) using Gaussian mixture output.
result MD-CGAN outperforms benchmarks, especially in noisy time series.
Study evaluates local explanation methods for time series forecasting.
problem Lack of local interpretability methods for multivariate time series forecasting.
method Proposed two novel evaluation metrics: Area Over the Perturbation Curve for Regression and Ablation Percentage Threshold.
result Comprehensive comparison of local explanation models on two datasets.
ABBA creates a new symbolic time series representation based on Brownian bridge.
problem Representing time series data in a compact, symbolic form.
method Adaptive polygonal chain approximation followed by mean-based clustering.
result ABBA outperforms other representations in preserving time series shape information.
This study compares deep learning models for multi-step dissolved oxygen prediction.
problem Lack of comprehensive comparison among deep learning models for multi-step time series forecasting.
method Walk-forward validation using real-time data from 2012 to 2016, tested models: CNN, TCN, LSTM, GRU, BiRNN.
result GRU outperforms other models in multi-step time series forecasting.
Deep learning transforms time series into images for anomaly detection in industrial assets.
problem Detecting anomalies in time series data from industrial assets.
method Transforming time series data into image-like representations and using them as inputs for deep learning models.
result Some encodings provide competitive results for anomaly detection in industrial asset monitoring.
Topological attention improves forecasting of univariate time series.
problem Forecasting univariate time series using local topological features.
method Topological attention mechanism that integrates local topological properties into forecasting models.
result Topological attention leads to state-of-the-art performance on the M4 benchmark.
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.
problem Challenges in financial time series forecasting, especially cross-sequence reasoning and multi-modal signals.
method Combines LLMs with financial data and news, using zero-shot/few-shot inference and instruction-based fine-tuning.
result LLMs can offer explainable financial forecasts, leveraging cross-sequence reasoning and multi-modal information.
MegazordNet combines stats and ML for better financial time series forecasting.
problem Forecasting financial time series is challenging due to its chaotic nature.
method MegazordNet integrates statistical features with a deep learning model.
result MegazordNet outperforms single statistical and machine learning methods in S&P 500 stock price prediction.
Simple mean and std-based classifier outperforms chance on 69 out of 128 time-series problems.
problem Time-series classification accuracy comparison
method Linear classifier using mean and standard deviation features
result Simple distributional features outperform chance on 69 out of 128 time-series problems
Combines CNN and Transformer for financial time series forecasting.
problem Forecasting financial time series, especially stock prices, is challenging due to short-term and long-term dependencies.
method Uses CNN for short-term dependencies and Transformer for long-term dependencies.
result Demonstrated superior performance in forecasting stock price changes compared to traditional methods.
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.
problem Detecting anomalies in noisy, complex time series data.
method Unsupervised model based on NVAE and GANs, transforming 1D to 2D images.
result T2IVAE outperforms other models on various datasets.
Proposes neural SDEs with change points for better time series modeling.
problem Restrictions in modeling time series with distributional shift.
method Generative adversarial networks (GANs) for SDEs and change point detection.
result Jointly learns change points and SDE model parameters.
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.
problem Handling multi-sensor time series with varying input dimensions.
method Graph neural network conditioning vectors for zero-shot transfer learning.
result Better generalization in activity recognition and equipment prognostics datasets.
This work compares OmniAnomaly with PCA for MTSAD, finding PCA can match or outperform OmniAnomaly.
problem Comparing deep learning models with classical methods in MTSAD under fair evaluation protocols.
method Systematic comparison of OmniAnomaly and PCA on SMD, using identical thresholding and evaluation procedures.
result PCA can achieve performance comparable to OmniAnomaly and even outperform it under certain conditions.
Inspired by trading, this method segments time series efficiently.
problem Time series segmentation for multivariate data.
method Normalize time series, treat each channel as a stock, use a posteriori trading signals for segmentation.
result Proposed method is faster and produces more intuitive results than existing models.
New algorithms predict causal links better than traditional methods in time series data.
problem Learning causal structure from time series data with challenges in real-world Earth sciences.
method Combination of established ideas for linear methods to identify causal links in non-linear systems, with a focus on large regression coefficients.
result Large regression coefficients can predict causal links better than small p-values in practice.
ElasTST improves time-series forecasting across varying horizons.
problem Robust forecasting across different time horizons in varied industrial sectors.
method Elastic Time-Series Transformer (ElasTST) with non-autoregressive design, rotary position embedding, and multi-scale patching.
result ElasTST provides robust forecasts across varying horizons without retraining.
This study compares deep generative models to traditional methods for generating financial time series.
problem Generating realistic multivariate financial time series for risk management and portfolio optimization.
method Systematic comparison of deep generative models (DGMs) against state-of-the-art parametric models on synthetic and empirical data.
result Deep generative models outperform traditional parametric models in generating financial time series.
Delay-SDE-net models time series with memory and uncertainty, outperforming other models.
problem Accurately modeling time series with memory and uncertainty.
method Stochastic delay differential equations (SDDEs) neural network model with aleatoric and epistemic uncertainty.
result The Delay-SDE-net consistently outperforms other models in predicting time series values and uncertainties.
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.
problem Challenges in time series forecasting due to evolving data distributions.
method Dynamic re-weighting of forecasters based on evolving data distributions.
result Competitive performance compared to state-of-the-art methods for combining 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.
problem Optimizing neural architectures for financial time series forecasting.
method Comparison of three NAS strategies (Bayesian optimization, hyperband, reinforcement learning) on chain-structured search spaces for simple and complex architectures.
result Bayesian optimization and hyperband outperform other strategies, and RNN and 1D CNN perform best among architectures.
Generative model for time series using Schrödinger bridges with jumps.
problem Creating realistic synthetic time series from observed data.
method Entropic optimal transport, Schrödinger bridge framework, jump-diffusion process.
result Jump-diffusion Schrödinger bridge model generates more realistic time series.