Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs?

• URL: http://arxiv.org/abs/2306.05035v3
• Date: Sun, 4 Feb 2024 04:42:26 GMT
• Title: Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs?
• Authors: Daojun Liang, Haixia Zhang, Dongfeng Yuan, Xiaoyan Ma, Dongyang Li and Minggao Zhang
• Abstract summary: Transformer-based models have achieved impressive performance on various time series tasks. Long-Term Series Forecasting (LTSF) tasks have also received extensive attention in recent years. Due to the inherent computational complexity and long sequences demanding of Transformer-based methods, its application on LTSF tasks still has two major issues.
• Score: 21.15722677855935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As Transformer-based models have achieved impressive performance on various time series tasks, Long-Term Series Forecasting (LTSF) tasks have also received extensive attention in recent years. However, due to the inherent computational complexity and long sequences demanding of Transformer-based methods, its application on LTSF tasks still has two major issues that need to be further investigated: 1) Whether the sparse attention mechanism designed by these methods actually reduce the running time on real devices; 2) Whether these models need extra long input sequences to guarantee their performance? The answers given in this paper are negative. Therefore, to better copy with these two issues, we design a lightweight Period-Attention mechanism (Periodformer), which renovates the aggregation of long-term subseries via explicit periodicity and short-term subseries via built-in proximity. Meanwhile, a gating mechanism is embedded into Periodformer to regulate the influence of the attention module on the prediction results. Furthermore, to take full advantage of GPUs for fast hyperparameter optimization (e.g., finding the suitable input length), a Multi-GPU Asynchronous parallel algorithm based on Bayesian Optimization (MABO) is presented. MABO allocates a process to each GPU via a queue mechanism, and then creates multiple trials at a time for asynchronous parallel search, which greatly reduces the search time. Compared with the state-of-the-art methods, the prediction error of Periodformer reduced by 13% and 26% for multivariate and univariate forecasting, respectively. In addition, MABO reduces the average search time by 46% while finding better hyperparameters. As a conclusion, this paper indicates that LTSF may not need complex attention and extra long input sequences. The code has been open sourced on Github.

Related papers

• POD-Attention: Unlocking Full Prefill-Decode Overlap for Faster LLM Inference [9.164093249308419]
  We present POD-Attention -- the first GPU kernel that efficiently computes attention for hybrid batches. POD-Attention aims to maximize the utilization of both compute and memory bandwidth by carefully allocating the GPU's resources.
  arXiv  Detail & Related papers  (2024-10-23T17:06:56Z)
• Parallel Decoding via Hidden Transfer for Lossless Large Language Model Acceleration [54.897493351694195]
  We propose a novel parallel decoding approach, namely textithidden transfer, which decodes multiple successive tokens simultaneously in a single forward pass. In terms of acceleration metrics, we outperform all the single-model acceleration techniques, including Medusa and Self-Speculative decoding.
  arXiv  Detail & Related papers  (2024-04-18T09:17:06Z)
• LongVQ: Long Sequence Modeling with Vector Quantization on Structured Memory [63.41820940103348]
  Self-attention mechanism's computational cost limits its practicality for long sequences. We propose a new method called LongVQ to compress the global abstraction as a length-fixed codebook. LongVQ effectively maintains dynamic global and local patterns, which helps to complement the lack of long-range dependency issues.
  arXiv  Detail & Related papers  (2024-04-17T08:26:34Z)
• Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer [36.75562615596186]
  We propose MASFormer, an easy-to-implement transformer variant with Mixed Attention Spans. MASFormer is equipped with full attention to capture long-range dependencies, but only at a small number of layers. Experiments show that a decoder-only MASFormer model of 1.3B parameters can achieve competitive performance to vanilla transformers with full attention.
  arXiv  Detail & Related papers  (2023-10-19T03:32:05Z)
• HyperAttention: Long-context Attention in Near-Linear Time [78.33061530066185]
  We present an approximate attention mechanism named HyperAttention to address the computational challenges posed by the growing complexity of long contexts. Empirically, employing Locality Sensitive Hashing (LSH) to identify large entries, HyperAttention outperforms existing methods. We validate the empirical performance of HyperAttention on a variety of different long-context length datasets.
  arXiv  Detail & Related papers  (2023-10-09T17:05:25Z)
• DBA: Efficient Transformer with Dynamic Bilinear Low-Rank Attention [53.02648818164273]
  We present an efficient yet effective attention mechanism, namely the Dynamic Bilinear Low-Rank Attention (DBA) DBA compresses the sequence length by input-sensitive dynamic projection matrices and achieves linear time and space complexity. Experiments over tasks with diverse sequence length conditions show that DBA achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-11-24T03:06:36Z)
• A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipelining [28.336502115532905]
  This paper proposes a coherent sequence length adaptive algorithm-hardware co-design for Transformer acceleration. We develop a hardware-friendly sparse attention operator and a length-aware hardware resource scheduling algorithm. Our design has very small accuracy loss and has 80.2 $times$ and 2.6 $times$ speedup compared to CPU and GPU implementation.
  arXiv  Detail & Related papers  (2022-08-07T05:48:38Z)
• SALO: An Efficient Spatial Accelerator Enabling Hybrid Sparse Attention Mechanisms for Long Sequences [16.332650428422443]
  We propose SALO to enable hybrid sparse attention mechanisms for long sequences. SALO contains a data scheduler to map hybrid sparse attention patterns onto hardware and a spatial accelerator. We show that SALO achieves 17.66x and 89.33x speedup on average compared to GPU and CPU implementations.
  arXiv  Detail & Related papers  (2022-06-29T12:01:19Z)
• Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Version [50.43914511877446]
  We propose a triangular, variable-specific attention to ensure high efficiency and accuracy. We show that Triformer outperforms state-of-the-art methods w.r.t. both accuracy and efficiency.
  arXiv  Detail & Related papers  (2022-04-28T20:41:49Z)
• Sketching as a Tool for Understanding and Accelerating Self-attention for Long Sequences [52.6022911513076]
  Transformer-based models are not efficient in processing long sequences due to the quadratic space and time complexity of the self-attention modules. We propose Linformer and Informer to reduce the quadratic complexity to linear (modulo logarithmic factors) via low-dimensional projection and row selection. Based on the theoretical analysis, we propose Skeinformer to accelerate self-attention and further improve the accuracy of matrix approximation to self-attention.
  arXiv  Detail & Related papers  (2021-12-10T06:58:05Z)
• Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting [25.417560221400347]
  Long sequence time-series forecasting (LSTF) demands a high prediction capacity. Recent studies have shown the potential of Transformer to increase the prediction capacity. We design an efficient transformer-based model for LSTF, named Informer, with three distinctive characteristics.
  arXiv  Detail & Related papers  (2020-12-14T11:43:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.