Hybrid LSTM-fully convolutional networks (LSTM-FCN) for time series classification have produced state-of-the-art classification results on univariate time series. We show that replacing the LSTM with a gated recurrent unit (GRU) to create a GRU-fully convolutional network hybrid model (GRU-FCN) can offer even better p…
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48 results for “univariate time series”
Deep learning methods improve time series forecasting by optimizing lag selection.
problem Optimizing the number of lags for accurate univariate time series forecasting.
method Empirical analysis of deep learning methods trained on multiple time series datasets.
result Excessively small or large lag sizes negatively impact forecasting performance.
AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecastingstat.ML
AdaPTS adapts univariate FMs for multivariate time series forecasting.
problem Challenges in managing feature dependencies and uncertainty quantification in multivariate time series forecasting.
method Adapters that transform multivariate inputs into a latent space and apply univariate FMs independently to each dimension.
result AdaPTS enhances forecasting accuracy and uncertainty quantification compared to baseline methods.
Deep Q-learning is investigated as an end-to-end solution to estimate the optimal strategies for acting on time series input. Experiments are conducted on two idealized trading games. 1) Univariate: the only input is a wave-like price time series, and 2) Bivariate: the input includes a random stepwise price time series…
New algorithms benchmarked for multivariate time series classification.
problem Comparing algorithms for multivariate time series classification.
method Review and comparison of recent MTSC algorithms using the UEA archive.
result HIVE-COTE ensemble is most accurate for MTSC, but dynamic time warping is competitive.
Time series anomaly detection plays a critical role in automated monitoring systems. Most previous deep learning efforts related to time series anomaly detection were based on recurrent neural networks (RNN). In this paper, we propose a time series segmentation approach based on convolutional neural networks (CNN) for …
An approach is proposed to determine structural shift in time-series assuming non-linear dependence of lagged values of dependent variable. Copulas are used to model non-linear dependence of time series components.
Graph Neural Networks improve financial time series forecasting accuracy.
problem Forecasting univariate financial time series with statistical significance.
method Introducing the Time-Geometric model combining geometric and temporal patterns.
result Statistically significant improvements in forecasting accuracy through geometric patterns.
A defining feature of non-stationary systems is the time dependence of their statistical parameters. Measured time series may exhibit Gaussian statistics on short time horizons, due to the central limit theorem. The sample statistics for long time horizons, however, averages over the time-dependent parameters. To model…
Anomalies in time-series data give essential and often actionable information in many applications. In this paper we consider a model-free anomaly detection method for univariate time-series which adapts to non-stationarity in the data stream and provides probabilistic abnormality scores based on the conformal predicti…
Dynamic functional time-series methods improve forecast accuracy for foreign exchange implied volatility surfaces.
problem Forecasting implied volatility surfaces in foreign exchange markets.
method Dynamic functional principal component analysis and multivariate functional time-series methods.
result Dynamic univariate functional time-series method shows the greatest improvement in forecast accuracy.
Point forecasting of univariate time series is a challenging problem with extensive work having been conducted. However, nonparametric probabilistic forecasting of time series, such as in the form of quantiles or prediction intervals is an even more challenging problem. In an effort to expand the possible forecasting p…
Global models outperform univariate benchmarks in complex time series forecasting.
problem Comparing global forecasting models to univariate benchmarks in various challenging scenarios.
method Simulated datasets with controlled characteristics, including homogeneity, complexity, and series lengths. Global forecasting models (RNN, LGBM) compared to univariate techniques.
result Global models like RNN and LGBM are competitive in complex scenarios with short series lengths and heterogeneous data.
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 -distance and employs deep learning for classification.
result Proposed method achieves superior classification performance compared to existing methods.
Paper uses topological data analysis for time series classification.
problem Classifying univariate time series data, especially physiological signals.
method Persistent homology for feature engineering, followed by machine learning.
result Higher accuracy achieved with fewer features compared to traditional methods.
In 2002, the UCR time series classification archive was first released with sixteen datasets. It gradually expanded, until 2015 when it increased in size from 45 datasets to 85 datasets. In October 2018 more datasets were added, bringing the total to 128. The new archive contains a wide range of problems, including var…
We use standard deep neural networks to classify univariate time series generated by discrete and continuous dynamical systems based on their chaotic or non-chaotic behaviour. Our approach to circumvent the lack of precise models for some of the most challenging real-life applications is to train different neural netwo…
New ensemble methods improve time series forecasting accuracy.
problem Global Forecasting Models (GFM) lack localisation for heterogeneous datasets.
method Ensemble techniques with clustering and varied GFM models.
result Significantly higher accuracy achieved compared to baseline models.
Time Series forecasting (univariate and multivariate) is a problem of high complexity due the different patterns that have to be detected in the input, ranging from high to low frequencies ones. In this paper we propose a new model for timeseries prediction that utilizes convolutional layers for feature extraction, a r…
Methodology to measure lag relevance in time series models.
problem Measuring lag relevance in machine learning models for univariate time series.
method Ghost variables, Shapley values, additive importance measures, auto-relevance and partial auto-relevance functions, one-step forecast.
result Calculated relevance measures successfully demonstrate expected lag structure in almost all cases.
The paper offers a unified approach to the study of three locally adaptive estimation methods in the context of univariate time series from both theoretical and empirical points of view. A general procedure for the computation of critical values is given. The underlying model encompasses all distributions from the expo…
Paper proposes MAST to identify stress conditions in forecasting models.
problem Improving reliability and transparency of univariate forecasting models under stress.
method Meta-learning and data augmentation approach to predict stress conditions.
result MAST identifies conditions leading to large errors in forecasting models.
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.
Neural GARCH models financial time series with time-varying coefficients.
problem Modeling conditional heteroskedasticity in financial time series.
method Neural network adaptation of GARCH and BEKK models with time-varying coefficients parameterized by a recurrent neural network.
result Neural Students t model consistently outperforms other models on financial time series.
With the advent of Big Data, nowadays in many applications databases containing large quantities of similar time series are available. Forecasting time series in these domains with traditional univariate forecasting procedures leaves great potentials for producing accurate forecasts untapped. Recurrent neural networks …
W-Transformers use wavelets to improve time series forecasting.
problem Forecasting non-stationary time series with long-range dependencies.
method Wavelet-based transformer architecture.
result W-Transformers outperform baseline models on various time series datasets.
RPE detects anomalies robustly in time-series data.
problem Detecting anomalies in time-series data efficiently and robustly.
method Window-based, robust projection step, closed-form algorithm.
result RPE can identify anomalies in time-stamp level and outperforms existing methods.
Survey evaluates 20 anomaly detection methods for time-series data.
problem Comparing statistical, machine learning, and deep learning methods for anomaly detection.
method Comprehensive evaluation of 20 methods from three categories on public datasets.
result Provides insights into the performance and suitability of anomaly detection methods.
Chronos-2 forecasts multivariate and covariate data without task-specific training.
problem Limited applicability of existing time series forecasting models to real-world multivariate and covariate data.
method Chronos-2 uses a group attention mechanism for in-context learning across multiple time series.
result Chronos-2 achieves state-of-the-art performance across comprehensive benchmarks.
ARM improves multivariate time series forecasting by better capturing series-wise relationships.
problem Challenges in handling complex temporal-contextual relationships in multivariate time series forecasting.
method ARM is an enhanced multivariate LTSF architecture that employs Adaptive Univariate Effect Learning, Random Dropping, and Multi-kernel Local Smoothing.
result ARM outperforms vanilla Transformers on multiple benchmarks without significantly increasing computational costs.
A new method for generating synthetic time series improves forecasting model accuracy.
problem Training forecasting models on imbalanced time series datasets.
method Data augmentation using oversampling strategies for imbalanced learning.
result The proposed method outperforms global and local models.
Sales forecasts are crucial for the E-commerce business. State-of-the-art techniques typically apply only univariate methods to make prediction for each series independently. However, due to the short nature of sales times series in E-commerce, univariate methods don't apply well. In this article, we propose a global m…
Hinge-FM2I fills missing data in time series with high accuracy.
problem Handling missing data in univariate time series.
method Inspired by door hinges, Hinge-FM2I imputes missing data using FM2I and selects the best imputed gap.
result Hinge-FM2I significantly outperforms other methods in sMAPE scores.
Study improves GFM accuracy with time series augmentation.
problem Limited time series data hinders GFM performance.
method Data augmentation techniques (GRATIS, MBB, DBA) and transfer learning.
result Significant improvement in GFM accuracy over baseline.
Chronos models improve financial forecasting by integrating multivariate data.
problem Improving financial forecasting accuracy using multivariate data.
method Evaluation of Chronos-2 on multivariate and univariate financial forecasting models.
result Multivariate forecasts consistently outperform univariate forecasts, especially for interest rates.
Quantifying Cryptocurrency Unpredictability: A Comprehensive Study of Complexity and Forecastingq-fin.ST
Cryptocurrency time-series predictability is low, resembling Brownian noise.
problem Low predictability of cryptocurrency exchange rates.
method Complexity and model predictions of Litecoin, Binance Coin, Bitcoin, Ethereum, and XRP exchange rates.
result Simpler models outperform complex ones in cryptocurrency forecasting.
Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time series data such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Auto…
Over the past decade, multivariate time series classification has received great attention. We propose transforming the existing univariate time series classification models, the Long Short Term Memory Fully Convolutional Network (LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN), into a multivariate time series classificat…
We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We develop our copula for first order Markov series, and extend it to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence…
CATS enhances MTSF by generating ATS from OTS to improve forecasting accuracy.
problem Recent deep learning models often outperform multivariate ones in MTSF.
method CATS constructs ATS from OTS using a 2D temporal-contextual attention mechanism.
result CATS achieves state-of-the-art performance with reduced complexity.
New method uses Transformers for flu forecasting.
problem Forecasting influenza-like illness trends.
method Transformer-based machine learning models with self-attention.
result Forecasting results are competitive with state-of-the-art methods.
This paper evaluates anomaly detection methods for multivariate time series data.
problem Lack of systematic comparison of anomaly detection methods on multivariate time series data.
method Comprehensive evaluation of 10 models and 4 scoring functions on 10 datasets.
result Dynamic scoring functions outperform static ones, and the choice of scoring functions matters more than the model choice.
Cisco introduces a new time series model for better forecasting.
problem Improving time series forecasting accuracy.
method Developed a new multiresolution decoder-only model trained on large datasets.
result The new model achieves superior performance on observability datasets.
Improves time series classification with forest proximities.
problem Time series classification accuracy and efficiency.
method PF-GAP, an extension of RF-GAP proximities to proximity forests, combined with Multi-Dimensional Scaling and Local Outlier Factors.
result Forest proximities show stronger connection between misclassified points and outliers.
Meta-learning framework improves zero-shot time-series forecasting.
problem Improving generalization on new time series from different datasets.
method Broad meta-learning framework with residual connections as adaptation mechanism.
result Viable zero-shot univariate forecasting without retraining.
Proposes a framework to explain complex global forecasting models.
problem Lack of interpretability in global forecasting models reduces stakeholder trust.
method Trains simpler univariate surrogate models on local forecasts of global models.
result Shows improved local model-agnostic interpretability of global forecasting models.
Bayesian Forecasting of Stock Returns on the JSE using Simultaneous Graphical Dynamic Linear Modelsq-fin.ST
Study forecasts stock returns on JSE using SGDLMs capturing cross-series dependencies.
problem Accurate forecasting of multivariate time series data.
method Simultaneous Graphical Dynamic Linear Models (SGDLMs) with customised DLMs and importance sampling/mean-field variational Bayes.
result SGDLMs accurately forecast stock data on JSE and respond to market changes.
Methods for detecting structural changes, or change points, in time series data are widely used in many fields of science and engineering. This chapter sketches some basic methods for the analysis of structural changes in time series data. The exposition is confined to retrospective methods for univariate time series. …