Papers Univariate Time Series Forecasting
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Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection
Multivariate Time Series Anomaly Detection (MTSAD) is essential for reliability and safety in domains such as industrial process monitoring and financial risk management, yet conventional approaches rely on application-s…
Univariate Time Series ForecastingTime Series Anomaly DetectionZero-shot GeneralizationZero-shot Multivariate Time Series Forecasting Using Tabular Prior Fitted Networks
Tabular foundation models, particularly Prior-data Fitted Networks like TabPFN have emerged as the leading contender in a myriad of tasks ranging from data imputation to label prediction on the tabular data format surpas…
Multivariate Time Series ForecastingUnivariate Time Series ForecastingAutomated univariate time series forecasting with regression trees
This paper describes a methodology for automated univariate time series forecasting using regression trees and their ensembles: bagging and random forests. The key aspects that are addressed are: the use of an autoregres…
Univariate Time Series ForecastingPredicting the Future by Retrieving the Past
Deep learning models such as MLP, Transformer, and TCN have achieved remarkable success in univariate time series forecasting, typically relying on sliding window samples from historical data for training. However, while…
Univariate Time Series ForecastingUnsupervised Anomaly Prediction with N-BEATS and Graph Neural Network in Multi-variate Semiconductor Process Time Series
Semiconductor manufacturing is an extremely complex and precision-driven process, characterized by thousands of interdependent parameters collected across diverse tools and process steps. Multi-variate time-series analys…
Univariate Time Series ForecastingGraph Neural NetworkAnomaly DetectionExploring Accuracy Law for Deep Time Series Forecasters: An Empirical Study
Deep time series forecasting has emerged as a rapidly growing field in recent years. Despite the exponential growth of community interests, progress on standard benchmarks is often limited to marginal improvements. A com…
Univariate Time Series ForecastingGateTS: Versatile and Efficient Forecasting via Attention-Inspired routed Mixture-of-Experts
Accurate univariate forecasting remains a pressing need in real-world systems, such as energy markets, hydrology, retail demand, and IoT monitoring, where signals are often intermittent and horizons span both short- and …
Univariate Time Series ForecastingComputational EfficiencyA Review of the Long Horizon Forecasting Problem in Time Series Analysis
The long horizon forecasting (LHF) problem has come up in the time series literature for over the last 35 years or so. This review covers aspects of LHF in this period and how deep learning has incorporated variants of t…
Multivariate Time Series ForecastingTime SeriesTime Series AnalysisUnivariate Time Series ForecastingModelRadar: Aspect-based Forecast Evaluation
Accurate evaluation of forecasting models is essential for ensuring reliable predictions. Current practices for evaluating and comparing forecasting models focus on summarising performance into a single score, using metr…
Time SeriesTime Series ForecastingUnivariate Time Series ForecastingAdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting
Pre-trained foundation models (FMs) have shown exceptional performance in univariate time series forecasting tasks. However, several practical challenges persist, including managing intricate dependencies among features …
Multivariate Time Series ForecastingRepresentation LearningTime SeriesTime Series Forecasting+2GenTL: A General Transfer Learning Model for Building Thermal Dynamics
Transfer Learning (TL) is an emerging field in modeling building thermal dynamics. This method reduces the data required for a data-driven model of a target building by leveraging knowledge from a source building. Conseq…
Deep LearningTransfer LearningUnivariate Time Series ForecastingTSFeatLIME: An Online User Study in Enhancing Explainability in Univariate Time Series Forecasting
Time series forecasting, while vital in various applications, often employs complex models that are difficult for humans to understand. Effective explainable AI techniques are crucial to bridging the gap between model pr…
Time SeriesTime Series ForecastingUnivariate Time Series ForecastingForecasting with Deep Learning: Beyond Average of Average of Average Performance
Accurate evaluation of forecasting models is essential for ensuring reliable predictions. Current practices for evaluating and comparing forecasting models focus on summarising performance into a single score, using metr…
Time Series ForecastingUnivariate Time Series ForecastingMeta-learning and Data Augmentation for Stress Testing Forecasting Models
The effectiveness of univariate forecasting models is often hampered by conditions that cause them stress. A model is considered to be under stress if it shows a negative behaviour, such as higher-than-usual errors or in…
Data AugmentationMeta-LearningTime SeriesTime Series Forecasting+1Lag Selection for Univariate Time Series Forecasting using Deep Learning: An Empirical Study
Most forecasting methods use recent past observations (lags) to model the future values of univariate time series. Selecting an adequate number of lags is important for training accurate forecasting models. Several appro…
Time SeriesTime Series ForecastingUnivariate Time Series ForecastingTFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
Time series are generated in diverse domains such as economic, traffic, health, and energy, where forecasting of future values has numerous important applications. Not surprisingly, many forecasting methods are being pro…
BenchmarkingMultivariate Time Series ForecastingTime SeriesTime Series Forecasting+1Leveraging Non-Decimated Wavelet Packet Features and Transformer Models for Time Series Forecasting
This article combines wavelet analysis techniques with machine learning methods for univariate time series forecasting, focusing on three main contributions. Firstly, we consider the use of Daubechies wavelets with diffe…
Time SeriesTime Series ForecastingUnivariate Time Series ForecastingMulti-horizon short-term load forecasting using hybrid of LSTM and modified split convolution
Precise short-term load forecasting (STLF) plays a crucial role in the smooth operation of power systems, future capacity planning, unit commitment, and demand response. However, due to its non-stationary and its dep…
Data AblationLoad ForecastingMissing ElementsMultivariate Time Series Forecasting+3PHILNet: A Novel Efficient Approach for Time Series Forecasting using Deep Learning
Time series is one of the most common data types in the industry nowadays. Forecasting the future of a time series behavior can be useful to plan ahead, save time, resources, and help avoid undesired scenarios. To make t…
Time SeriesTime Series ForecastingUnivariate Time Series ForecastingA New Deep Learning Architecture withInductive Bias Balance for Transformer Oil Temperature Forecasting
Ensuring optimal performance of power transformers is a laborious task, where the insulation system is essential to decrease their deterioration. The insulation system uses the insulate oil required to control temperatur…
Inductive BiasTime SeriesTime Series ForecastingUnivariate Time Series Forecasting