2026· Poster Volume 0008 The 2026 Twenty-Second International Conference on Intelligent Computing July 23-26, 2026 Toronto, Canada· 0 citations
TL;DR
The Decomposed Recurrent Neural Network (DeRNN) is proposed, which decouples global trend modeling from local fluctuation extraction via an asymmetric dual-track architecture and exhibits superior robustness against noise and distribution shifts.
Abstract
Long-Term Time Series Forecasting (LTSF) is pivotal in domains like energy and traffic management but necessitates capturing intricate dependencies over extended windows. While Transformer-based models dominate, they suffer from quadratic complexity and positional insensitivity. Conversely, recent lightweight MLP/RNN-based models often forcibly compress conflicting dynamic features—linear trends and non-linear fluctuations—into a single channel, leading to suboptimal accuracy. To address these limitations, we propose the Decomposed Recurrent Neural Network (DeRNN). Our approach decouples global trend modeling from local fluctuation extraction via an asymmetric dual-track architecture. Specifically, we introduce a Trend Anchor Track to preserve global scale via direct linear projection, and a Seasonal Feature Track utilizing Bi-directional GRUs to capture complex non-linear dependencies within a reversible normalized space. Extensive experiments on seven benchmarks demonstrate that DeRNN achieves highly competitive, and in most cases superior, accuracy against state-of-the-art methods while maintaining extremely low latency and memory usage. Furthermore, the model exhibits superior robustness against noise and distribution shifts.
Forecasting time series over long horizons is essential for proactive decision-making in many systems. Recent research has focused on transformer-based architectures, which capture long-range dependencies in sequential data. However, several studies show that simpler linear models can outperform transformers by avoiding overfitting during training. In this context, we present NeuroFlexMLP, a deep learning model for multivariate time series forecasting tasks. NeuroFlexMLP's key distinct feature is the adaptability to the diverse complexity of real-world time series, which is achieved, from the architecture standpoint, by adding non-linear residual blocks to a first linear block. This architectural design simplifies hyperparameter optimization, leading to accurate forecasts for various time series data types regardless of the lookback or prediction horizons, outperforming state-of-the-art (SOTA) models on challenging real-world datasets. Its Multi-Layer Perceptron (MLP) design ensures high computational efficiency, making it scalable for longer input sequences than transformer-based models. We validate NeuroFlexMLP for the LEO satellite beam hopping use case, where its lightweight design enables on-board deployment, and on state-of-the art AI datasets. Across all these benchmarks, NeuroFlexMLP achieves competitive accuracy over state-of-the-art models while providing an adaptive architecture that significantly reduces computational overhead. On the LEO beam hopping task, it achieves up to 35.9% MSE reduction over Informer, which translates into up to 28% lower provisioning cost under asymmetric cost models that penalize under-allocation more heavily than over-allocation.
P. F. Pérez, Claudio Fiandrino, Marco Fiore et al.· La Main· 0 citations
Forecasting financial indices remains difficult because market observations combine persistent movements, short-lived disturbances, nonlinear interactions, and substantial noise. A single recurrent architecture may retain nearby temporal information yet fail to represent widely separated dependencies, whereas an attention-based encoder is effective at relating distant positions but does not by itself guarantee detailed sensitivity to local sequence dynamics. This paper reformulates the forecasting pipeline as a cascaded global–local learning problem. A Transformer encoder first converts normalized price windows into contextual representations through positional encoding, multi-head self-attention, residual normalization, and a feedforward sublayer. Those representations are then processed by a bidirectional long short-term memory network, so forward and reverse recurrent states refine the global context before a dense prediction head produces the output. The design was assessed on daily closing-price series for the S&P 500, Dow Jones Industrial Average, and Nasdaq Composite over 2 September 2003–13 July 2023. Preprocessing included interpolation of missing observations, interquartile-range screening of outliers, first differencing, min–max scaling, and windowed sample construction. Tests against recurrent, bidirectional recurrent, feedforward, Informer, and temporal-convolution baselines show that the hybrid system delivers the strongest overall error and goodness-of-fit profile across the three markets. The findings indicate that passing attention-derived context into a bidirectional memory module offers a practical means of combining long-horizon structure with local temporal variation, although computational cost remains relevant for latency-sensitive trading applications.
Long-term time series forecasting requires models that can represent smooth global trends and irregular local fluctuations without assigning the same capacity to both. Existing decomposition-based neural forecasters often separate signals but continue to process the resulting components with architecturally homogeneous encoders, which can overfit simple trends and underfit nonlinear residuals. This paper proposes MLPFormer, a hybrid MLP–Transformer framework for component-specific forecasting. The input sequence is decomposed by a moving-average filter into trend and residual terms. A shallow channel-wise MLP encodes the low-frequency trend, providing a low-capacity inductive bias for smooth structures, whereas a Transformer encoder with depthwise separable convolution models high-frequency residual dynamics and cross-variable dependencies. The two paths are fused and decoded by parallel linear and nonlinear prediction heads. Experiments on nine public benchmarks, with results reported as the mean over three random seeds, show average reductions of 11.8% in MSE and 8.1% in MAE relative to the mean of seven competitive baselines spanning Transformer-based, linear/MLP-based, and RNN-based paradigms published between 2023 and 2025. Ablation studies confirm that the gains arise from matching encoder capacity to component complexity rather than from increasing parameter count. MLPFormer provides a practical hybrid artificial intelligence design for forecasting tasks in which trend and residual structures coexist.
The extreme non-stationarity, high noise levels, and multi-timescale coupling in financial futures markets pose major challenges for time series forecasting. Existing models often struggle to disentangle localized shocks from global trends due to incompatible inductive biases. To address this issue, we propose a Decomposition-Enhanced Network (DENet). Following a divide-and-conquer paradigm, DENet adopts a multi-stream architecture: the main path extracts stable trends via moving averages and dual-path linear projections, while Auxiliary Stream I captures seasonal and local cyclical patterns using depthwise separable convolutions, and Auxiliary Stream II models high-frequency dynamics through a nonlinear autoregressive-style mapping. These components are integrated via an adaptive fusion mechanism, balancing global robustness and local structural sensitivity. Experiments on real-world futures data demonstrate that DENet outperforms a wide range of state-of-the-art benchmarks. Compared with seven state-of-the-art baselines, DENet achieves an average reduction of 10.15% in RMSE for daily forecasting on iron ore futures, and an average reduction of 22.57% in MAE under the 5-min, 12-step forecasting horizon. Furthermore, we integrate DENet’s dual-scale predictions into the R-Breaker strategy with parameter switching and dynamic position sizing. Backtesting results reveal that the annualized return of iron ore futures outperforms the baseline strategy by an average of 7.6 percentage points across five backtesting windows. Ultimately, DENet effectively bridges advanced structural modeling and actionable algorithmic trading.
Time series forecasting (TSF) is vital to many applications, yet existing models often struggle to capture the heterogeneous long-range global patterns and short-range local variations in multivariate time series. While some approaches partially model these dependencies, they often do not jointly exploit temporal and feature-wise information. To address this challenge, we propose HyBDM, a multi-scale hybrid model that decomposes temporal dynamics into global patterns and local variations, which are modeled by two specialized experts. The Global Patterns Expert employs an enhanced BiConv-Mamba module that integrates bidirectional convolutions, an M-SSM layer, a forgetting mechanism, and a GDD-MLP module for cross-channel modeling. The Local Variations Expert uses a Local Window Transformer (LWT) to perform efficient locality-aware attention with reduced computational complexity. In addition, a Multi-Scale Patcher and a Long-Short Router enable multi-resolution representations and adaptive fusion of the two experts. Experiments on six benchmark datasets show that HyBDM outperforms state-of-the-art methods in both forecasting accuracy and computational efficiency, demonstrating its effectiveness in bridging global-local dependencies for multivariate TSF.
Wenqiang Ma, Chen Cheng, Xue Cheng et al.· 0 citations
Probabilistic time series forecasting remains challenging, largely because modeling distinct trend and seasonal dynamics requires specialized approaches. Existing methods often fail to capture the unique inner properties of these components, lack interpretability, or suffer from heavy memory and runtime overhead. To address these limitations, we propose DecoVAE, a lightweight interpretable trend-seasonal VAE framework that explicitly decomposes time series into trend and seasonal components by applying domain-specific inductive biases. The trend stream enforces structural smoothness using a differential regularizer on the latent trajectory, analogous to the Hodrick-Prescott filter. Concurrently, the seasonal stream operates in the frequency domain via a complex Gaussian VAE, natively capturing the amplitude and phase of periodic patterns. Extensive evaluations across seven real-world benchmarks show that DecoVAE consistently outperforms strong baselines. It achieves reductions of up to 14.96\% in CRPS and 23.30\% in NMAE for short-term forecasting, and up to 52.68\% and 26.51\% for long-term horizons. Crucially, DecoVAE yields these accuracy gains while remaining highly efficient, reducing model weight by up to 93\% and accelerating speed by up to 74\% compared to the second-best method.
A. Marusov, D. Anikin, Alexey Zaytsev· 0 citations