PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformer
Recently, the superiority of Transformer for long-term time series forecasting (LTSF) tasks has been challenged, particularly since recent work has shown that simple models can outperform numerous Transformer-based approaches. This suggests that a notable gap remains in fully leveraging the potential of Transformer in LTSF tasks. Consequently, this study investigates key issues when applying Transformer to LTSF, encompassing aspects of temporal continuity, information density, and multi-channel relationships. We introduce the Placeholder-enhanced Technique (PET) to enhance the computational efficiency and predictive accuracy of Transformer in LTSF tasks. Furthermore, we delve into the impact of larger patch strategies and channel interaction strategies on Transformer's performance, specifically Long Sub-sequence Division (LSD) and Multi-channel Separation and Interaction (MSI). These strategies collectively constitute a novel model termed PETformer. Extensive experiments have demonstrated that PETformer achieves state-of-the-art performance on eight commonly used public datasets for LTSF, surpassing all existing models. The insights and enhancement methodologies presented in this paper serve as valuable reference points and sources of inspiration for future research endeavors.
Code (1)
Tasks
Computational EfficiencyMultivariate Time Series ForecastingTime SeriesTime Series ForecastingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
MPPN: Multi-Resolution Periodic Pattern Network For Long-Term Time Series Forecasting
Long-term time series forecasting plays an important role in various real-world scenarios. Recent deep learning methods for long-term series forecasting tend to capture the intricate patterns of time series by decomposit…
Time SeriesTime Series ForecastingLong-term Forecasting with TiDE: Time-series Dense Encoder
Recent work has shown that simple linear models can outperform several Transformer based approaches in long term time-series forecasting. Motivated by this, we propose a Multi-layer Perceptron (MLP) based encoder-decoder…
Anomaly DetectionDecoderTime SeriesTime Series ForecastingNot All Frequencies Are Created Equal:Towards a Dynamic Fusion of Frequencies in Time-Series Forecasting
Long-term time series forecasting is a long-standing challenge in various applications. A central issue in time series forecasting is that methods should expressively capture long-term dependency. Furthermore, time serie…
AllTime SeriesTime Series ForecastingTimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting
Non-stationarity poses significant challenges for multivariate time series forecasting due to the inherent short-term fluctuations and long-term trends that can lead to spurious regressions or obscure essential long-term…
Multivariate Time Series ForecastingTime SeriesTime Series ForecastingMulti-Scale Dilated Convolution Network for Long-Term Time Series Forecasting
Accurate forecasting of long-term time series has important applications for decision making and planning. However, it remains challenging to capture the long-term dependencies in time series data. To better extract long…
Decision MakingTime SeriesTime Series Forecasting