{"arxiv_id":"2310.20496","incoming":{"papers":1,"sentences":1,"groups":[{"kind":"background","label":"Cites it as background","papers":1,"sentences":1,"quotes":[{"arxiv_id":"2605.18068","title":"Teger: Spatiotemporal Covariance for Probabilistic Traffic Forecasting","url":"/paper/2605.18068","section":"Related Work","sentence":"For instance, [ref] proposes a spatio-temporal attention model, others leverage trend-aware decomposition in the definition of the attention layer [ref] .","intent_p":0.92,"contests_p":null}]}],"unlabelled_sentences":0},"outgoing":{"papers":12,"sentences":36,"groups":[{"kind":"comparison","label":"Compares with","papers":4,"sentences":7,"quotes":[{"arxiv_id":"1803.01271","title":"An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling","url":"/paper/1803.01271","section":"Experiments","sentence":"Models for comparison: In this study, we compare our proposed model against the following state-of-the-art models: four transformer-based models, namely FEDformer [ref] , Autoformer [ref] , Pyraformer [ref] ; one MLP-based model, i.e., Dlinear [ref] ; and one CNN-based model, i.e., TCN [ref] .","intent_p":1.0,"contests_p":null},{"arxiv_id":"2201.12740","title":"FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting","url":"/paper/2201.12740","section":"Experiments","sentence":"Models for comparison: In this study, we compare our proposed model against the following state-of-the-art models: four transformer-based models, namely FEDformer [ref] , Autoformer [ref] , Pyraformer [ref] ; one MLP-based model, i.e., Dlinear [ref] ; and one CNN-based model, i.e., TCN [ref] .","intent_p":1.0,"contests_p":null},{"arxiv_id":"2205.13504","title":"Are Transformers Effective for Time Series Forecasting?","url":"/paper/2205.13504","section":"Experiments","sentence":"Models for comparison: In this study, we compare our proposed model against the following state-of-the-art models: four transformer-based models, namely FEDformer [ref] , Autoformer [ref] , Pyraformer [ref] ; one MLP-based model, i.e., Dlinear [ref] ; and one CNN-based model, i.e., TCN [ref] .","intent_p":1.0,"contests_p":null},{"arxiv_id":"2201.12886","title":"N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting","url":"/paper/2201.12886","section":"Experiments","sentence":"We also consider two recently proposed models, e.g., N-Hits [ref] and FiLM [ref] .","intent_p":0.99,"contests_p":null}]},{"kind":"background","label":"Cites as background","papers":8,"sentences":8,"quotes":[{"arxiv_id":"2201.12886","title":"N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting","url":"/paper/2201.12886","section":"Related works","sentence":"The first group involves N-BEATS [ref] , N-HiTS [ref] , and FiLM [ref] .","intent_p":0.91,"contests_p":null},{"arxiv_id":"1905.10437","title":"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting","url":"/paper/1905.10437","section":"Related works","sentence":"The first group involves N-BEATS [ref] , N-HiTS [ref] , and FiLM [ref] .","intent_p":0.91,"contests_p":null},{"arxiv_id":"2205.13504","title":"Are Transformers Effective for Time Series Forecasting?","url":"/paper/2205.13504","section":"Related works","sentence":"On the other hand, the models based on learnable bases, such as Dlinear [ref] , TCN [ref] , Deepar [ref] , LogTrans [ref] , Informer [ref] , AutoFormer [ref] , FedFormer [ref] , etc, use a learnable linear or convolution layer, or covariate embeddings as the basis.","intent_p":0.84,"contests_p":null},{"arxiv_id":"1803.01271","title":"An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling","url":"/paper/1803.01271","section":"Related works","sentence":"On the other hand, the models based on learnable bases, such as Dlinear [ref] , TCN [ref] , Deepar [ref] , LogTrans [ref] , Informer [ref] , AutoFormer [ref] , FedFormer [ref] , etc, use a learnable linear or convolution layer, or covariate embeddings as the basis.","intent_p":0.84,"contests_p":null},{"arxiv_id":"2201.12740","title":"FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting","url":"/paper/2201.12740","section":"Related works","sentence":"On the other hand, the models based on learnable bases, such as Dlinear [ref] , TCN [ref] , Deepar [ref] , LogTrans [ref] , Informer [ref] , AutoFormer [ref] , FedFormer [ref] , etc, use a learnable linear or convolution layer, or covariate embeddings as the basis.","intent_p":0.84,"contests_p":null}]}],"unlabelled_sentences":21},"claims":[],"thresholds":{"intent_p":0.8,"supports_p":0.95,"contests_p":0.7},"method":"Citing sentences come from each paper's LaTeX source; every \\cite marks the sentence and the works it cites. Pith's classifier typed each sentence. A label is shown at intent probability 0.8 or more (0.95 for support), a contest at 0.7 or more. Measured accuracy against two blind model families is at https://pith.science/accuracy. The quoted sentences are the citing authors' own.","held_up":null,"_links":{"html":"https://pith.science/paper/2310.20496"}}