{"total":16,"rows":[{"id":"2106.13008","title":"Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting","created":"2021-06-24","cited_by_count":1676,"area":null},{"id":"2205.14415","title":"Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting","created":"2022-05-28","cited_by_count":391,"area":null},{"id":"2301.03028","title":"Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement","created":"2023-01-08","cited_by_count":90,"area":null},{"id":"2212.03523","title":"Criteria for Classifying Forecasting Methods","created":"2022-12-07","cited_by_count":48,"area":null},{"id":"2102.05829","title":"Causal Inference for Time series Analysis: Problems, Methods and Evaluation","created":"2021-02-11","cited_by_count":42,"area":null},{"id":"2212.01209","title":"FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting","created":"2022-12-02","cited_by_count":15,"area":null},{"id":"2110.14354","title":"MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data","created":"2021-10-27","cited_by_count":9,"area":null},{"id":"2108.12784","title":"TCCT: Tightly-Coupled Convolutional Transformer on Time Series Forecasting","created":"2021-08-29","cited_by_count":8,"area":null},{"id":"2306.05035","title":"Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs?","created":"2023-06-08","cited_by_count":5,"area":null},{"id":"2405.10877","title":"WEITS: A Wavelet-enhanced residual framework for interpretable time series forecasting","created":"2024-05-17","cited_by_count":3,"area":null},{"id":"2312.14280","title":"Fine-grained Forecasting Models Via Gaussian Process Blurring Effect","created":"2023-12-21","cited_by_count":2,"area":null},{"id":"2409.03986","title":"An Efficient and Generalizable Symbolic Regression Method for Time Series Analysis","created":"2024-09-06","cited_by_count":2,"area":null},{"id":"2408.16109","title":"A nudge to the truth: atom conservation as a hard constraint in models of atmospheric composition using an uncertainty-weighted correction","created":"2024-08-28","cited_by_count":0,"area":null},{"id":"2504.17913","title":"CANet: ChronoAdaptive Network for Enhanced Long-Term Time Series Forecasting under Non-Stationarity","created":"2025-04-24","cited_by_count":0,"area":null},{"id":"2511.19657","title":"Structured Noise Modeling for Enhanced Time-Series Forecasting","created":"2025-11-24","cited_by_count":0,"area":null},{"id":"2603.21724","title":"FISformer: Replacing Self-Attention with a Fuzzy Inference System in Transformer Models for Time Series Forecasting","created":"2026-03-23","cited_by_count":0,"area":null}],"next_cursor":null}