AdaRDiff is a new adaptive reversible differencing technique for time‑series forecasting that learns weighted differencing to remove trend and seasonality, stabilizes residuals for forecasting, and then reconstructs the forecast autoregressively. The method offers a closed‑form convolutional implementation that can be GPU‑parallelized, achieving up to 33.7× speedup over naive recurrence. Experiments on eight diverse benchmarks show state‑of‑the‑art accuracy and significant performance gains when integrated into various backbone models, from linear models to Transformers.
arXiv:2601. 16632v4 Announce Type: replace-cross Abstract: Time series forecasting has witnessed significant progress with deep learning.
arXiv:2606. 10678v1 Announce Type: new Abstract: Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture long-range dependencies.
arXiv:2603. 15506v2 Announce Type: replace-cross Abstract: We argue that the current practice of evaluating AI/ML time-series forecasting models, predominantly on benchmarks characterized by strong, persistent periodicities and seasonalities, obscures real progress by overlooking the performance of efficient classical methods.
arXiv:2607. 09537v1 Announce Type: new Abstract: Time series forecasting requires models to capture diverse, often mutually exclusive, temporal dynamics, from smooth trend continuation to nonstationary drift and strict phase-aligned recurrence.
Irregular multivariate time series forecasting (IMTSF) requires capturing temporal dynamics and cross-variable dependencies from sparse, asynchronous observations. Existing methods often rely on learn...
arXiv:2607. 00197v1 Announce Type: new Abstract: Long-horizon multivariate time series forecasting (LTSF) remains challenging due to non-stationarity, regime shifts, and error accumulation.
arXiv:2501. 04339v2 Announce Type: replace-cross Abstract: We introduce the Deep Convolutional Interpreter for Time Series (DCIts), a deep-learning architecture for nonlinear multivariate time series that provides sample-specific, locally interpretable descriptions of the underlying interaction structure.
arXiv:2609.15344v1 Announce Type: new Abstract: We study the adaptation of pretrained language models to univariate time-series forecasting through a parameter-efficient transfer learning framework,...
GateTS introduces a streamlined univariate forecasting model that merges sparse Mixture-of-Experts computation with an attention-inspired gating mechanism, eliminating the need for auxiliary load‑balancing losses. The architecture achieves superior predictive accuracy across both short‑ and long‑term horizons, including intermittent signals, while using far fewer parameters than leading transformer models such as PatchTST. Empirical tests on diverse datasets show GateTS is computationally more efficient than LSTM, enabling cost‑effective inference for real‑world time‑series applications.
arXiv:2605. 11287v2 Announce Type: replace-cross Abstract: A persistent paradox in time-series forecasting is that structurally simple MLP and linear models often outperform high-capacity Transformers.
arXiv:2606. 27282v1 Announce Type: new Abstract: Time-series forecasting research has been moving steadily toward larger architectures, from specialized transformers to general-purpose foundation models, on the assumption that capacity is what unlocks accuracy.
arXiv:2505. 15354v3 Announce Type: replace Abstract: Time-series forecasting is a critical task in various business domains, but it remains inherently challenging.