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Found 56 papers, 19 papers with code
Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt
For the state-of-the-art (SOTA) model, the MSE is reduced by $33. 3\%$.
Attention as Robust Representation for Time Series Forecasting
Time series forecasting is essential for many practical applications, with the adoption of transformer-based models on the rise due to their impressive performance in NLP and CV.
FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting
Current research predominantly relies on historical solar power data or numerical weather prediction in a single-modality format, ignoring the complementary information provided in different modalities.
Sparse-VQ Transformer: An FFN-Free Framework with Vector Quantization for Enhanced Time Series Forecasting
Time series analysis is vital for numerous applications, and transformers have become increasingly prominent in this domain.
Computational Efficiency
Multivariate Time Series Forecasting
+2
RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies
Time series forecasting is an important and forefront task in many real-world applications.
DSAF: A Dual-Stage Adaptive Framework for Numerical Weather Prediction Downscaling
While widely recognized as one of the most substantial weather forecasting methodologies, Numerical Weather Prediction (NWP) usually suffers from relatively coarse resolution and inevitable bias due to tempo-spatial discretization, physical parametrization process, and computation limitation.
SVQ: Sparse Vector Quantization for Spatiotemporal Forecasting
Moreover, we approximate the sparse regression process using a blend of a two-layer MLP and an extensive codebook.
Ranked #5 on
Traffic Prediction
on BJTaxi
One Fits All: Universal Time Series Analysis by Pretrained LM and Specially Designed Adaptors
Despite the impressive achievements of pre-trained models in the fields of natural language processing (NLP) and computer vision (CV), progress in the domain of time series analysis has been limited.
Interactive Generalized Additive Model and Its Applications in Electric Load Forecasting
Electric load forecasting is an indispensable component of electric power system planning and management.
WeatherGNN: Exploiting Complicated Relationships in Numerical Weather Prediction Bias Correction
In addition, the interactions between weather factors are further complicated by the spatial dependencies between regions, which are influenced by varied terrain and atmospheric motions.
OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data.
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition
More importantly, almost all methods assume the observations are sampled at regular time stamps, and fail to handle complex irregular sampled time series arising from different applications.
Benchmarks and Custom Package for Electrical Load Forecasting
Based on this, we conducted extensive experiments on load data at different levels, providing a reference for researchers to compare different load forecasting models.
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
On the other hand, contrastive learning aims to find a representation that can clearly distinguish any instance from the others, which can bring a more natural and promising representation for time series anomaly detection.
GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting
On the other hand, the long input sequence usually leads to large model size and high time complexity.
SaDI: A Self-adaptive Decomposed Interpretable Framework for Electric Load Forecasting under Extreme Events
In this paper, we propose a novel forecasting framework, named Self-adaptive Decomposed Interpretable framework~(SaDI), which ensembles long-term trend, short-term trend, and period modelings to capture temporal characteristics in different components.
DiffLoad: Uncertainty Quantification in Load Forecasting with Diffusion Model
The uncertainties in load forecasting can be divided into two types: epistemic uncertainty and aleatoric uncertainty.
AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes
This paper discusses horizontal POD resources management in Alibaba Cloud Container Services with a newly deployed AI algorithm framework named AHPA -- the adaptive horizontal pod auto-scaling system.
Robust Dominant Periodicity Detection for Time Series with Missing Data
In this paper, we propose a robust and effective periodicity detection algorithm for time series with block missing data.
One Fits All:Power General Time Series Analysis by Pretrained LM
The main challenge that blocks the development of pre-trained model for time series analysis is the lack of a large amount of data for training.
Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment
The goal of sequential event prediction is to estimate the next event based on a sequence of historical events, with applications to sequential recommendation, user behavior analysis and clinical treatment.
TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis
Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data.
TreeDRNet:A Robust Deep Model for Long Term Time Series Forecasting
Various deep learning models, especially some latest Transformer-based approaches, have greatly improved the state-of-art performance for long-term time series forecasting. However, those transformer-based models suffer a severe deterioration performance with prolonged input length, which prohibits them from using extended historical info. Moreover, these methods tend to handle complex examples in long-term forecasting with increased model complexity, which often leads to a significant increase in computation and less robustness in performance(e. g., overfitting).
Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach
The learning problem is framed as a subset selection task in which a subset of all possible rules needs to be selected to form an accurate and interpretable rule set.
Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection
Dynamic time warping (DTW) is an effective dissimilarity measure in many time series applications.
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they effectively utilize historical information.
Ranked #3 on
Time Series Forecasting
on ETTh2 (96) Univariate
NetRCA: An Effective Network Fault Cause Localization Algorithm
Localizing the root cause of network faults is crucial to network operation and maintenance.
Transformers in Time Series: A Survey
From the perspective of network structure, we summarize the adaptations and modifications that have been made to Transformers in order to accommodate the challenges in time series analysis.
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
Although Transformer-based methods have significantly improved state-of-the-art results for long-term series forecasting, they are not only computationally expensive but more importantly, are unable to capture the global view of time series (e. g. overall trend).
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms
As business of Alibaba expands across the world among various industries, higher standards are imposed on the service quality and reliability of big data cloud computing platforms which constitute the infrastructure of Alibaba Cloud.
A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition
Many real-world time series exhibit multiple seasonality with different lengths.
Uncertainty-Aware Task Allocation for Distributed Autonomous Robots
This paper addresses task-allocation problems with uncertainty in situational awareness for distributed autonomous robots (DARs).
Two-Stage Framework for Seasonal Time Series Forecasting
By incorporating the learned long-range structure, the second stage can enhance the prediction accuracy in the forecast horizon.
Text-Embedded Bilinear Model for Fine-Grained Visual Recognition
Specially, we first conduct a text-embedded network to embed text feature into the discriminative image feature learning to get a embedded feature.
Fine-Grained Image Recognition
Fine-Grained Visual Recognition
+1
An Explainable 3D Residual Self-Attention Deep Neural Network FOR Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI
Different from the existing approaches, the novelty of our approach is three-fold: 1) A Residual Self-Attention Deep Neural Network has been proposed to capture local, global and spatial information of MR images to improve diagnostic performance; 2) An explanation method using Gradient-based Localization Class Activation mapping (Grad-CAM) has been introduced to improve the explainable of the proposed method; 3) This work has provided a full end-to-end learning solution for automated disease diagnosis.
Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT
We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP).
Time Series Data Augmentation for Deep Learning: A Survey
In this paper, we systematically review different data augmentation methods for time series.
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks
It is deployed as a public online service and widely adopted in different business scenarios at Alibaba Group.
RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity Detection
Periodicity detection is a crucial step in time series tasks, including monitoring and forecasting of metrics in many areas, such as IoT applications and self-driving database management system.
Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China
First, we trained 1D CNNs, LSTM RNNs, and GRU RNNs based on the full images’ time series to attain three classifiers with optimal architectures and hyper-parameters.
Exploring Overall Contextual Information for Image Captioning in Human-Like Cognitive Style
In the training process, the forward and backward LSTMs encode the succeeding and preceding words into their respective hidden states by simultaneously constructing the whole sentence in a complementary manner.
RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering
Extracting the underlying trend signal is a crucial step to facilitate time series analysis like forecasting and anomaly detection.
Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement
We validate the superiority of the proposed method in our real-time high precision positioning system against several popular state-of-the-art robust regression methods.
Multimodal Semantic Attention Network for Video Captioning
Inspired by the fact that different modalities in videos carry complementary information, we propose a Multimodal Semantic Attention Network(MSAN), which is a new encoder-decoder framework incorporating multimodal semantic attributes for video captioning.
Which Factorization Machine Modeling is Better: A Theoretical Answer with Optimal Guarantee
This bound is sub-optimal comparing to the information theoretical lower bound $\mathcal{O}(kd)$.
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component.
Cross-Technology Communications for Heterogeneous IoT Devices Through Artificial Doppler Shifts
To this end, we propose DopplerFi, a communication framework that enables a two-way communication channel between BLE and Wi-Fi by injecting artificial Doppler shifts, which can be decoded by sensing the patterns in the Gaussian frequency shift keying (GFSK) demodulator and Channel State Information (CSI).
Networking and Internet Architecture
A Many-Objective Evolutionary Algorithm With Two Interacting Processes: Cascade Clustering and Reference Point Incremental Learning
This paper proposes a many-objective optimization algorithm with two interacting processes: cascade clustering and reference point incremental learning (CLIA).
Fill it up: Exploiting partial dependency annotations in a minimum spanning tree parser
Unsupervised models of dependency parsing typically require large amounts of clean, unlabeled data plus gold-standard part-of-speech tags.
Expectation-maximization for logistic regression
We present a family of expectation-maximization (EM) algorithms for binary and negative-binomial logistic regression, drawing a sharp connection with the variational-Bayes algorithm of Jaakkola and Jordan (2000).
Projection onto A Nonnegative Max-Heap
In this paper, we show that such Euclidean projection problem admits an analytical solution and we develop a top-down algorithm where the key operation is to find the so-called \emph{maximal root-tree} of the subtree rooted at each node.
Efficient Recovery of Jointly Sparse Vectors
MMV is an extension of the single measurement vector (SMV) model employed in standard compressive sensing (CS).
Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data
Recent advances in neuroimaging techniques provide great potentials for effective diagnosis of Alzheimer’s disease (AD), the most common form of dementia.
Multi-label Multiple Kernel Learning
We present a multi-label multiple kernel learning (MKL) formulation, in which the data are embedded into a low-dimensional space directed by the instance-label correlations encoded into a hypergraph.
