model_cards = dict( nhitsm={ "Abstract": ( "The N-HiTS_M incorporates hierarchical interpolation and multi-rate data sampling " "techniques. It assembles its predictions sequentially, selectively emphasizing " "components with different frequencies and scales, while decomposing the input signal " " and synthesizing the forecast [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, " "Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski. N-HiTS: Neural " "Hierarchical Interpolation for Time Series Forecasting, Submitted working paper.]" "(https://arxiv.org/abs/2201.12886)" ), "Intended use": ( "The N-HiTS_M model specializes in monthly long-horizon forecasting by improving " "accuracy and reducing the training time and memory requirements of the model." ), "Secondary use": ( "The interpretable predictions of the model produce a natural frequency time " "series signal decomposition." ), "Limitations": ( "The transferability across different frequencies has not yet been tested, it is " "advisable to restrict the use of N-HiTS_{M} to monthly data were it was pre-trained. " "This model purely autorregresive, transferability of models with exogenous variables " "is yet to be done." ), "Training data": ( "N-HiTS_M was trained on 48,000 monthly series from the M4 competition " "[Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. The " " M4 competition: 100,000 time series and 61 forecasting methods. International " "Journal of Forecasting, 36(1):54–74, 2020. ISSN 0169-2070.]" "(https://www.sciencedirect.com/science/article/pii/S0169207019301128)" ), "Citation Info": ( "@article{challu2022nhits,\n " "author = {Cristian Challu and \n" " Kin G. Olivares and \n" " Boris N. Oreshkin and \n" " Federico Garza and \n" " Max Mergenthaler and \n" " Artur Dubrawski}, \n " "title = {N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting},\n " "journal = {Computing Research Repository},\n " "volume = {abs/2201.12886},\n " "year = {2022},\n " "url = {https://arxiv.org/abs/2201.12886},\n " "eprinttype = {arXiv},\n " "eprint = {2201.12886},\n " "biburl = {https://dblp.org/rec/journals/corr/abs-2201-12886.bib}\n}" ), }, nhitsh={ "Abstract": ( "The N-HiTS_{H} incorporates hierarchical interpolation and multi-rate data sampling " "techniques. It assembles its predictions sequentially, selectively emphasizing " "components with different frequencies and scales, while decomposing the input signal " " and synthesizing the forecast [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, " "Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski. N-HiTS: Neural " "Hierarchical Interpolation for Time Series Forecasting, Submitted working paper.]" "(https://arxiv.org/abs/2201.12886)" ), "Intended use": ( "The N-HiTS_{H} model specializes in hourly long-horizon forecasting by improving " "accuracy and reducing the training time and memory requirements of the model." ), "Secondary use": ( "The interpretable predictions of the model produce a natural frequency time " "series signal decomposition." ), "Limitations": ( "The transferability across different frequencies has not yet been tested, it is " "advisable to restrict the use of N-HiTS_{H} to hourly data were it was pre-trained. " "This model purely autorregresive, transferability of models with exogenous variables " "is yet to be done." ), "Training data": ( "N-HiTS_{H} was trained on 414 hourly series from the M4 competition " "[Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. The " " M4 competition: 100,000 time series and 61 forecasting methods. International " "Journal of Forecasting, 36(1):54–74, 2020. ISSN 0169-2070.]" "(https://www.sciencedirect.com/science/article/pii/S0169207019301128)" ), "Citation Info": ( "@article{challu2022nhits,\n " "author = {Cristian Challu and \n" " Kin G. Olivares and \n" " Boris N. Oreshkin and \n" " Federico Garza and \n" " Max Mergenthaler and \n" " Artur Dubrawski}, \n " "title = {N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting},\n " "journal = {Computing Research Repository},\n " "volume = {abs/2201.12886},\n " "year = {2022},\n " "url = {https://arxiv.org/abs/2201.12886},\n " "eprinttype = {arXiv},\n " "eprint = {2201.12886},\n " "biburl = {https://dblp.org/rec/journals/corr/abs-2201-12886.bib}\n}" ), }, nhitsd={ "Abstract": ( "The N-HiTS_D incorporates hierarchical interpolation and multi-rate data sampling " "techniques. It assembles its predictions sequentially, selectively emphasizing " "components with different frequencies and scales, while decomposing the input signal " " and synthesizing the forecast [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, " "Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski. N-HiTS: Neural " "Hierarchical Interpolation for Time Series Forecasting, Submitted working paper.]" "(https://arxiv.org/abs/2201.12886)" ), "Intended use": ( "The N-HiTS_D model specializes in daily long-horizon forecasting by improving " "accuracy and reducing the training time and memory requirements of the model." ), "Secondary use": ( "The interpretable predictions of the model produce a natural frequency time " "series signal decomposition." ), "Limitations": ( "The transferability across different frequencies has not yet been tested, it is " "advisable to restrict the use of N-HiTS_D to daily data were it was pre-trained. " "This model purely autorregresive, transferability of models with exogenous variables " "is yet to be done." ), "Training data": ( "N-HiTS_D was trained on 4,227 daily series from the M4 competition " "[Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. The " " M4 competition: 100,000 time series and 61 forecasting methods. International " "Journal of Forecasting, 36(1):54–74, 2020. ISSN 0169-2070.]" "(https://www.sciencedirect.com/science/article/pii/S0169207019301128)" ), "Citation Info": ( "@article{challu2022nhits,\n " "author = {Cristian Challu and \n" " Kin G. Olivares and \n" " Boris N. Oreshkin and \n" " Federico Garza and \n" " Max Mergenthaler and \n" " Artur Dubrawski}, \n " "title = {N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting},\n " "journal = {Computing Research Repository},\n " "volume = {abs/2201.12886},\n " "year = {2022},\n " "url = {https://arxiv.org/abs/2201.12886},\n " "eprinttype = {arXiv},\n " "eprint = {2201.12886},\n " "biburl = {https://dblp.org/rec/journals/corr/abs-2201-12886.bib}\n}" ), }, nhitsy={ "Abstract": ( "The N-HiTS_Y incorporates hierarchical interpolation and multi-rate data sampling " "techniques. It assembles its predictions sequentially, selectively emphasizing " "components with different frequencies and scales, while decomposing the input signal " " and synthesizing the forecast [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, " "Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski. N-HiTS: Neural " "Hierarchical Interpolation for Time Series Forecasting, Submitted working paper.]" "(https://arxiv.org/abs/2201.12886)" ), "Intended use": ( "The N-HiTS_Y model specializes in yearly long-horizon forecasting by improving " "accuracy and reducing the training time and memory requirements of the model." ), "Secondary use": ( "The interpretable predictions of the model produce a natural frequency time " "series signal decomposition." ), "Limitations": ( "The transferability across different frequencies has not yet been tested, it is " "advisable to restrict the use of N-HiTS_Y to yearly data were it was pre-trained. " "This model purely autorregresive, transferability of models with exogenous variables " "is yet to be done." ), "Training data": ( "N-HiTS_{H} was trained on 23,000 yearly series from the M4 competition " "[Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. The " " M4 competition: 100,000 time series and 61 forecasting methods. International " "Journal of Forecasting, 36(1):54–74, 2020. ISSN 0169-2070.]" "(https://www.sciencedirect.com/science/article/pii/S0169207019301128)" ), "Citation Info": ( "@article{challu2022nhits,\n " "author = {Cristian Challu and \n" " Kin G. Olivares and \n" " Boris N. Oreshkin and \n" " Federico Garza and \n" " Max Mergenthaler and \n" " Artur Dubrawski}, \n " "title = {N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting},\n " "journal = {Computing Research Repository},\n " "volume = {abs/2201.12886},\n " "year = {2022},\n " "url = {https://arxiv.org/abs/2201.12886},\n " "eprinttype = {arXiv},\n " "eprint = {2201.12886},\n " "biburl = {https://dblp.org/rec/journals/corr/abs-2201-12886.bib}\n}" ), }, nbeatsm={ "Abstract": ( "The N-BEATS_M models is a model based on a deep stack multi-layer percentrons connected" "with doubly residual connections. The model combines a multi-step forecasting strategy " "with projections unto piecewise functions for its generic version or polynomials and " "harmonics for its interpretable version. [Boris N. Oreshkin, Dmitri Carpov, Nicolas " "Chapados, Yoshua Bengio. N-BEATS: Neural basis expansion analysis for interpretable " "time series forecasting. 8th International Conference on Learning Representations, " "ICLR 2020.](https://arxiv.org/abs/1905.10437)" ), "Intended use": ( "The N-BEATS_M is an efficient univariate forecasting model specialized in monthly " "data, that uses the multi-step forecasting strategy." ), "Secondary use": ( "The interpretable variant of N-BEATSi_M produces a trend and seasonality " "decomposition." ), "Limitations": ( "The transferability across different frequencies has not yet been tested, it is " "advisable to restrict the use of N-BEATS_M to monthly data were it was pre-trained." "This model purely autorregresive, transferability of models with exogenous variables " "is yet to be done." ), "Training data": ( "N-BEATS_M was trained on 48,000 monthly series from the M4 competition " "[Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. The " " M4 competition: 100,000 time series and 61 forecasting methods. International " "Journal of Forecasting, 36(1):54–74, 2020. ISSN 0169-2070.]" "(https://www.sciencedirect.com/science/article/pii/S0169207019301128)" ), "Citation Info": ( "@inproceedings{oreshkin2020nbeats,\n " "author = {Boris N. Oreshkin and \n" " Dmitri Carpov and \n" " Nicolas Chapados and\n" " Yoshua Bengio},\n " "title = {{N-BEATS:} Neural basis expansion analysis for interpretable time series forecasting},\n " "booktitle = {8th International Conference on Learning Representations, {ICLR} 2020},\n " "year = {2020},\n " "url = {https://openreview.net/forum?id=r1ecqn4YwB}\n }" ), }, nbeatsh={ "Abstract": ( "The N-BEATS_H models is a model based on a deep stack multi-layer percentrons connected" "with doubly residual connections. The model combines a multi-step forecasting strategy " "with projections unto piecewise functions for its generic version or polynomials and " "harmonics for its interpretable version. [Boris N. Oreshkin, Dmitri Carpov, Nicolas " "Chapados, Yoshua Bengio. N-BEATS: Neural basis expansion analysis for interpretable " "time series forecasting. 8th International Conference on Learning Representations, " "ICLR 2020.](https://arxiv.org/abs/1905.10437)" ), "Intended use": ( "The N-BEATS_H is an efficient univariate forecasting model specialized in hourly " "data, that uses the multi-step forecasting strategy." ), "Secondary use": ( "The interpretable variant of N-BEATSi_H produces a trend and seasonality " "decomposition." ), "Limitations": ( "The transferability across different frequencies has not yet been tested, it is " "advisable to restrict the use of N-BEATS_H to hourly data were it was pre-trained." "This model purely autorregresive, transferability of models with exogenous variables " "is yet to be done." ), "Training data": ( "N-BEATS_H was trained on 414 hourly series from the M4 competition " "[Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. The " " M4 competition: 100,000 time series and 61 forecasting methods. International " "Journal of Forecasting, 36(1):54–74, 2020. ISSN 0169-2070.]" "(https://www.sciencedirect.com/science/article/pii/S0169207019301128)" ), "Citation Info": ( "@inproceedings{oreshkin2020nbeats,\n " "author = {Boris N. Oreshkin and \n" " Dmitri Carpov and \n" " Nicolas Chapados and\n" " Yoshua Bengio},\n " "title = {{N-BEATS:} Neural basis expansion analysis for interpretable time series forecasting},\n " "booktitle = {8th International Conference on Learning Representations, {ICLR} 2020},\n " "year = {2020},\n " "url = {https://openreview.net/forum?id=r1ecqn4YwB}\n }" ), }, nbeatsd={ "Abstract": ( "The N-BEATS_D models is a model based on a deep stack multi-layer percentrons connected" "with doubly residual connections. The model combines a multi-step forecasting strategy " "with projections unto piecewise functions for its generic version or polynomials and " "harmonics for its interpretable version. [Boris N. Oreshkin, Dmitri Carpov, Nicolas " "Chapados, Yoshua Bengio. N-BEATS: Neural basis expansion analysis for interpretable " "time series forecasting. 8th International Conference on Learning Representations, " "ICLR 2020.](https://arxiv.org/abs/1905.10437)" ), "Intended use": ( "The N-BEATS_D is an efficient univariate forecasting model specialized in hourly " "data, that uses the multi-step forecasting strategy." ), "Secondary use": ( "The interpretable variant of N-BEATSi_D produces a trend and seasonality " "decomposition." ), "Limitations": ( "The transferability across different frequencies has not yet been tested, it is " "advisable to restrict the use of N-BEATS_D to daily data were it was pre-trained." "This model purely autorregresive, transferability of models with exogenous variables " "is yet to be done." ), "Training data": ( "N-BEATS_D was trained on 4,227 daily series from the M4 competition " "[Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. The " " M4 competition: 100,000 time series and 61 forecasting methods. International " "Journal of Forecasting, 36(1):54–74, 2020. ISSN 0169-2070.]" "(https://www.sciencedirect.com/science/article/pii/S0169207019301128)" ), "Citation Info": ( "@inproceedings{oreshkin2020nbeats,\n " "author = {Boris N. Oreshkin and \n" " Dmitri Carpov and \n" " Nicolas Chapados and\n" " Yoshua Bengio},\n " "title = {{N-BEATS:} Neural basis expansion analysis for interpretable time series forecasting},\n " "booktitle = {8th International Conference on Learning Representations, {ICLR} 2020},\n " "year = {2020},\n " "url = {https://openreview.net/forum?id=r1ecqn4YwB}\n }" ), }, nbeatsw={ "Abstract": ( "The N-BEATS_W models is a model based on a deep stack multi-layer percentrons connected" "with doubly residual connections. The model combines a multi-step forecasting strategy " "with projections unto piecewise functions for its generic version or polynomials and " "harmonics for its interpretable version. [Boris N. Oreshkin, Dmitri Carpov, Nicolas " "Chapados, Yoshua Bengio. N-BEATS: Neural basis expansion analysis for interpretable " "time series forecasting. 8th International Conference on Learning Representations, " "ICLR 2020.](https://arxiv.org/abs/1905.10437)" ), "Intended use": ( "The N-BEATS_W is an efficient univariate forecasting model specialized in weekly " "data, that uses the multi-step forecasting strategy." ), "Secondary use": ( "The interpretable variant of N-BEATSi_W produces a trend and seasonality " "decomposition." ), "Limitations": ( "The transferability across different frequencies has not yet been tested, it is " "advisable to restrict the use of N-BEATS_W to weekly data were it was pre-trained." "This model purely autorregresive, transferability of models with exogenous variables " "is yet to be done." ), "Training data": ( "N-BEATS_W was trained on 359 weekly series from the M4 competition " "[Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. The " " M4 competition: 100,000 time series and 61 forecasting methods. International " "Journal of Forecasting, 36(1):54–74, 2020. ISSN 0169-2070.]" "(https://www.sciencedirect.com/science/article/pii/S0169207019301128)" ), "Citation Info": ( "@inproceedings{oreshkin2020nbeats,\n " "author = {Boris N. Oreshkin and \n" " Dmitri Carpov and \n" " Nicolas Chapados and\n" " Yoshua Bengio},\n " "title = {{N-BEATS:} Neural basis expansion analysis for interpretable time series forecasting},\n " "booktitle = {8th International Conference on Learning Representations, {ICLR} 2020},\n " "year = {2020},\n " "url = {https://openreview.net/forum?id=r1ecqn4YwB}\n }" ), }, nbeatsy={ "Abstract": ( "The N-BEATS_Y models is a model based on a deep stack multi-layer percentrons connected" "with doubly residual connections. The model combines a multi-step forecasting strategy " "with projections unto piecewise functions for its generic version or polynomials and " "harmonics for its interpretable version. [Boris N. Oreshkin, Dmitri Carpov, Nicolas " "Chapados, Yoshua Bengio. N-BEATS: Neural basis expansion analysis for interpretable " "time series forecasting. 8th International Conference on Learning Representations, " "ICLR 2020.](https://arxiv.org/abs/1905.10437)" ), "Intended use": ( "The N-BEATS_Y is an efficient univariate forecasting model specialized in hourly " "data, that uses the multi-step forecasting strategy." ), "Secondary use": ( "The interpretable variant of N-BEATSi_Y produces a trend and seasonality " "decomposition." ), "Limitations": ( "The transferability across different frequencies has not yet been tested, it is " "advisable to restrict the use of N-BEATS_Y to yearly data were it was pre-trained." "This model purely autorregresive, transferability of models with exogenous variables " "is yet to be done." ), "Training data": ( "N-BEATS_Y was trained on 23,000 yearly series from the M4 competition " "[Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. The " " M4 competition: 100,000 time series and 61 forecasting methods. International " "Journal of Forecasting, 36(1):54–74, 2020. ISSN 0169-2070.]" "(https://www.sciencedirect.com/science/article/pii/S0169207019301128)" ), "Citation Info": ( "@inproceedings{oreshkin2020nbeats,\n " "author = {Boris N. Oreshkin and \n" " Dmitri Carpov and \n" " Nicolas Chapados and\n" " Yoshua Bengio},\n " "title = {{N-BEATS:} Neural basis expansion analysis for interpretable time series forecasting},\n " "booktitle = {8th International Conference on Learning Representations, {ICLR} 2020},\n " "year = {2020},\n " "url = {https://openreview.net/forum?id=r1ecqn4YwB}\n }" ), }, arima={ "Abstract": ( "The AutoARIMA model is a classic autoregressive model that automatically explores ARIMA" "models with a step-wise algorithm using Akaike Information Criterion. It applies to " "seasonal and non-seasonal data and has a proven record in the M3 forecasting competition. " "An efficient open-source version of the model was only available in R but is now also " "available in Python. [StatsForecast: Lightning fast forecasting with statistical and " "econometric models](https://github.com/Nixtla/statsforecast)." ), "Intended use": ( "The AutoARIMA is an univariate forecasting model, intended to produce automatic " "predictions for large numbers of time series." ), "Secondary use": ( "It is a classical model and is an almost obligated forecasting baseline." ), "Limitations": ( "ARIMA model uses a recurrent prediction strategy. It concatenates errors on long " "horizon forecasting settings. It is a fairly simple model that does not model " "non-linear relationships." ), "Training data": ( "The AutoARIMA is a univariate model that uses only autorregresive data from " "the target variable." ), "Citation Info": ( "@article{hyndman2008auto_arima," "title={Automatic Time Series Forecasting: The forecast Package for R},\n" "author={Hyndman, Rob J. and Khandakar, Yeasmin},\n" "volume={27},\n" "url={https://www.jstatsoft.org/index.php/jss/article/view/v027i03},\n" "doi={10.18637/jss.v027.i03},\n" "number={3},\n" "journal={Journal of Statistical Software},\n" "year={2008},\n" "pages={1–22}\n" "}" ), }, exp_smoothing={ "Abstract": ( "Exponential smoothing is a classic technique using exponential window functions, " "and one of the most successful forecasting methods. It has a long history, the " "name was coined by Charles C. Holt. [Holt, Charles C. (1957). Forecasting Trends " 'and Seasonal by Exponentially Weighted Averages". Office of Naval Research ' "Memorandum.](https://www.sciencedirect.com/science/article/abs/pii/S0169207003001134)." ), "Intended use": ( "Simple variants of exponential smoothing can serve as an efficient baseline method." ), "Secondary use": ( "The exponential smoothing method can also act as a low-pass filter removing " "high-frequency noise. " ), "Limitations": ( "The method can face limitations if the series show strong discontinuities, or if " "the high-frequency components are an important part of the predicted signal." ), "Training data": ( "Just like the ARIMA method, exponential smoothing uses only autorregresive data " " from the target variable." ), "Citation Info": ( "@article{holt1957exponential_smoothing, \n" "title = {Forecasting seasonals and trends by exponentially weighted moving averages},\n" "author = {Charles C. Holt},\n" "journal = {International Journal of Forecasting},\n" "volume = {20},\n" "number = {1},\n" "pages = {5-10}\n," "year = {2004(1957)},\n" "issn = {0169-2070},\n" "doi = {https://doi.org/10.1016/j.ijforecast.2003.09.015},\n" "url = {https://www.sciencedirect.com/science/article/pii/S0169207003001134},\n" "}" ), }, prophet={ "Abstract": ( "Prophet is a widely used forecasting method. Prophet is a nonlinear regression model." ), "Intended use": ("Prophet can serve as a baseline method."), "Secondary use": ( "The Prophet model is also useful for time series decomposition." ), "Limitations": ( "The method can face limitations if the series show strong discontinuities, or if " "the high-frequency components are an important part of the predicted signal." ), "Training data": ( "Just like the ARIMA method and exponential smoothing, Prophet uses only autorregresive data " " from the target variable." ), "Citation Info": ( "@article{doi:10.1080/00031305.2017.1380080,\n" "author = {Sean J. Taylor and Benjamin Letham},\n" "title = {Forecasting at Scale},\n" "journal = {The American Statistician},\n" "volume = {72},\n" "number = {1},\n" "pages = {37-45},\n" "year = {2018},\n" "publisher = {Taylor & Francis},\n" "doi = {10.1080/00031305.2017.1380080},\n" "URL = {https://doi.org/10.1080/00031305.2017.1380080},\n" "eprint = {https://doi.org/10.1080/00031305.2017.1380080},\n" "}" ), }, )