{"type":"ScholarlyArticle","title":"Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting","authors":[{"name":"Wei Fan","slug":"wei-fan","given":"Wei","family":"Fan","affiliation":"Medicial Sciences Division , University of Oxford"},{"name":"Kun Yi","slug":"kun-yi","given":"Kun","family":"Yi","affiliation":"School of Computer Science and Technology , Beijing Institute of Technology"},{"name":"Hangting Ye","slug":"hangting-ye","given":"Hangting","family":"Ye","affiliation":"School of Artificial Intelligence , Jilin University"},{"name":"Zhiyuan Ning","slug":"zhiyuan-ning","given":"Zhiyuan","family":"Ning","affiliation":"Computer Network Information Center , Chinese Academy of Science"},{"name":"Qi Zhang","slug":"qi-zhang","given":"Qi","family":"Zhang","affiliation":"Department of Computer Science and Technology , Tongji University"},{"name":"Ning An","slug":"ning-an","given":"Ning","family":"An","affiliation":"School of Computer Science and Information Engineering , Hefei University of Technology"}],"abstract":"While most time series are non-stationary, it is inevitable for models to face the distribution shift issue in time series forecasting. Existing solutions manipulate statistical measures (usually mean and std.) to adjust time series distribution. However, these operations can be theoretically seen as the transformation towards zero frequency component of the spectrum which cannot reveal full distribution information and would further lead to information utilization bottleneck in normalization, thus hindering forecasting performance. To address this problem, we propose to utilize the whole frequency spectrum to transform time series to make full use of data distribution from the frequency perspective. We present a deep frequency derivative learning framework, DERITS, for non-stationary time series forecasting. Specifically, DERITS is built upon a novel reversible transformation, namely Frequency Derivative Transformation (FDT) that makes signals derived in the frequency domain to acquire more stationary frequency representations. Then, we propose the Order-adaptive Fourier Convolution Network to conduct adaptive frequency filtering and learning. Furthermore, we organize DERITS as a parallel-stacked architecture for the multi-order derivation and fusion for forecasting. Finally, we conduct extensive experiments on several datasets which show the consistent superiority in both time series forecasting and shift alleviation.","doi":"10.24963/ijcai.2024/436","url":"https://ijcai.org/proceedings/2024/436","pdf":"https://ijcai.org/proceedings/2024/0436.pdf","markdown":"https://ijcai.org/proceedings/2024/436.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/436","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":436,"pages":{"first":3944,"last":3952},"keywords":["Data Mining: DM: Mining spatial and/or temporal data","Machine Learning: ML: Time series and data streams"],"videos":[],"references":[{"position":1,"raw":"Models LTSF-Linear +RevIN +Dish-TS +FDT Metrics MAE RMSE MAE RMSE MAE RMSE MAE RMSE Exchange 96 0.038 0.052 0.040 0.055 0.039 0.053 0.036 0.050 192 0.053 0.069 0.052 0.070 0.055 0.071 0.050 0.068 336 0.064 0.085 0.069 0.094 0.068 0.090 0.060 0.082 720 0.092 0.116 0.115 0.145 0.110 0.132 0.090 0.114 Weather 96 0.040 0.081 0.042 0.085 0.039 0.082 0.037 0.080 192 0.048 0.089 0.045 0.089 0.046 0.090 0.043 0.088 336 0.056 0.098 0.053 0.097 0.055 0.099 0.050 0.095 720 0.065 0.106 0.061 0.108 0.060 0.105 0.056 0.103 ILI 24 0.167 0.214 0.151 0.199 0.156 0.203 0.141 0.196 36 0.179 0.231 0.168 0.228 0.171 0.230 0.158 0.212 48 0.165 0.216 0.158 0.214 0.160 0.214 0.151 0.198 60 0.166 0.212 0.161 0.204 0.164 0.210 0.152 0.196 Electricity 96 0.045 0.075 0.046 0.078 0.044 0.074 0.041 0.072 192 0.043 0.070 0.042 0.070 0.043 0.071 0.040 0.068 336 0.044 0.071 0.043 0.070 0.042 0.070 0.040 0.067 720 0.054 0.080 0.048 0.076 0.050 0.077 0.048 0.076 ETTm1 96 0.055 0.080 0.054 0.078 0.052 0.077 0.051 0.072 192 0.060 0.087 0.058 0.086 0.057 0.085 0.056 0.084 336 0.065 0.093 0.062 0.090 0.064 0.092 0.060 0.088 720 0.072 0.099 0.070 0.100 0.071 0.102 0.066 0.093","title":"Models LTSF-Linear +RevIN +Dish-TS +FDT Metrics MAE RMSE MAE RMSE MAE","authors":null,"year":null,"venue":"RMSE MAE RMSE Exchange","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Ariyo et al., 2014] Adebiyi A Ariyo, Adewumi O Adewumi, and Charles K Ayo. 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