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Found 22 papers, 11 papers with code
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
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
Model-free Test Time Adaptation for Out-Of-Distribution Detection
We demonstrate the effectiveness of \abbr through comprehensive experiments on multiple OOD detection benchmarks, extensive empirical studies show that \abbr significantly improves the performance of OOD detection over state-of-the-art methods.
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.
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.
CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting
In this work, we design a special Transformer, i. e., Channel Aligned Robust Blend Transformer (CARD for short), that addresses key shortcomings of CI type Transformer in time series forecasting.
How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?
In this paper, we establish a theoretical framework that unravels the expressive power of spectral-temporal GNNs.
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.
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).
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
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).
Mid-flight Forecasting for CPA Lines in Online Advertising
For Verizon MediaDemand Side Platform(DSP), forecasting of ad campaign performance not only feeds key information to the optimization server to allow the system to operate on a high-performance mode, but also produces actionable insights to the advertisers.
An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions
Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions.
Bid Shading by Win-Rate Estimation and Surplus Maximization
We found that bid shading, in general, can deliver significant value to advertisers, reducing price per impression to about 55% of the unshaded cost.
Bid Shading in The Brave New World of First-Price Auctions
Online auctions play a central role in online advertising, and are one of the main reasons for the industry's scalability and growth.
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving
To address the issue of significantly increased serving delay and high memory usage for ad serving in production, this paper presents \emph{DeepLight}: a framework to accelerate the CTR predictions in three aspects: 1) accelerate the model inference via explicitly searching informative feature interactions in the shallow component; 2) prune redundant layers and parameters at intra-layer and inter-layer level in the DNN component; 3) promote the sparsity of the embedding layer to preserve the most discriminant signals.
Ranked #7 on
Click-Through Rate Prediction
on Avazu
