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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"
"}"
),
},
)
|