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Time Series Forecasting has been an active area of research due to its many applications ranging from network usage prediction, resource allocation, anomaly detection, and predictive maintenance.
1912
Earlier work this paper cites.
D. M. Allen, “Mean square error of prediction as a criterion for selecting variables,” Technometrics , vol. 13, no. 3, pp. 469–475, 1971
1971
Earlier work this paper cites.
R. L. Chambers and R. Dunstan, “Estimating distribution functions from survey data,” Biometrika , vol. 73, no. 3, pp. 597–604, 1986
1986
Earlier work this paper cites.
R. Fildes and S. Makridakis, “Forecasting and loss functions,” International Journal of Forecasting , vol. 4, no. 4, pp. 545–550, 1988
1988
Earlier work this paper cites.
S. Makridakis and M. Hibon, “Exponential smoothing: The effect of initial values and loss functions on post-sample forecasting accuracy,” International Journal of Forecasting , vol. 7, no. 3, pp. 317–330, 1991
1991
Earlier work this paper cites.
J. Lin, “Divergence measures based on the shannon entropy,” IEEE Transactions on Information theory , vol. 37, no. 1, pp. 145–151, 1991
1991
Earlier work this paper cites.
R. Fildes, “The evaluation of extrapolative forecasting methods,” International Journal of Forecasting , vol. 8, no. 1, pp. 81–98, 1992
1992
Earlier work this paper cites.
B. F. Arnold and P. Stahlecker, “Some properties of the relative squared error approach to linear regression analysis,” Statistics , vol. 38, no. 3, pp. 183–193, 2004
2004
Earlier work this paper cites.
L. Rosasco, E. De Vito, A. Caponnetto, M. Piana, and A. Verri, “Are loss functions all the same?” Neural computation , vol. 16, no. 5, pp. 1063–1076, 2004
2004
Earlier work this paper cites.
J. G. De Gooijer and R. J. Hyndman, “25 years of time series forecasting,” International journal of forecasting , vol. 22, no. 3, pp. 443–473, 2006
2006
Earlier work this paper cites.
I. Steinwart, “How to compare different loss functions and their risks,” Constructive Approximation , vol. 26, no. 2, pp. 225–287, 2007
2007
Earlier work this paper cites.
T.-H. Lee, “Loss functions in time series forecasting,” International encyclopedia of the social sciences , pp. 495–502, 2008
2008
Earlier work this paper cites.
M. Cuturi, “PEMS-SF,” UCI Machine Learning Repository, 2011
2011
Cited alongside, same era.
C. Massmann and H. Holzmann, “Analysing goodness of fit measures using a sensitivity based approach,” in EGU General Assembly Conference Abstracts , 2012, p. 12354
2012
Cited alongside, same era.
M. V. Shcherbakov, A. Brebels, N. L. Shcherbakova, A. P. Tyukov, T. A. Janovsky, V. A. Kamaev et al. , “A survey of forecast error measures,” World applied sciences journal , vol. 24, no. 24, pp. 171–176, 2013
2013
Cited alongside, same era.
S. Laurent, J. V. Rombouts, and F. Violante, “On loss functions and ranking forecasting performances of multivariate volatility models,” Journal of Econometrics , vol. 173, no. 1, pp. 1–10, 2013
2013
Cited alongside, same era.
T. Chai and R. R. Draxler, “Root mean square error (rmse) or mean absolute error (mae)?–arguments against avoiding rmse in the literature,” Geoscientific model development , vol. 7, no. 3, pp. 1247–1250, 2014
J. Heaton, “Applications of deep neural networks,” 2020
2020
Later among the works it cites.
S. Jadon, “A survey of loss functions for semantic segmentation,” in 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) . IEEE, 2020, pp. 1–7
2020
Later among the works it cites.
G. P. Meyer, “An alternative probabilistic interpretation of the huber loss,” in Proceedings of the ieee/cvf conference on computer vision and pattern recognition , 2021, pp. 5261–5269
2021
Later among the works it cites.
S. Jadon, A. Patankar, and J. K. Milczek, “Challenges and approaches to time-series forecasting for traffic prediction at data centers,” in 2021 International Conference on Smart Applications, Communications and Networking (SmartNets) . IEEE, 2021, pp. 1–8
2021
Later among the works it cites.
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2014
Cited alongside, same era.
A. Trindade, “ElectricityLoadDiagrams20112014,” UCI Machine Learning Repository, 2015
2015
Cited alongside, same era.
N. G. Reich, J. Lessler, K. Sakrejda, S. A. Lauer, S. Iamsirithaworn, and D. A. Cummings, “Case study in evaluating time series prediction models using the relative mean absolute error,” The American Statistician , vol. 70, no. 3, pp. 285–292, 2016
2016
Cited alongside, same era.
A. De Myttenaere, B. Golden, B. Le Grand, and F. Rossi, “Mean absolute percentage error for regression models,” Neurocomputing , vol. 192, pp. 38–48, 2016
2016
Cited alongside, same era.
S. T. Kim, H.-I. Jeong, and F.-F. Jin, “Mean bias in seasonal forecast model and enso prediction error,” Scientific reports , vol. 7, no. 1, pp. 1–9, 2017
2017
Cited alongside, same era.
R. Koenker, V. Chernozhukov, X. He, and L. Peng, “Handbook of quantile regression,” 2017
2017
Cited alongside, same era.
J. Qi, J. Du, S. M. Siniscalchi, X. Ma, and C.-H. Lee, “On mean absolute error for deep neural network based vector-to-vector regression,” IEEE Signal Processing Letters , vol. 27, pp. 1485–1489, 2020. [Online]. Available: https://doi.org/10.1109%2Flsp.2020.3016837
2020
Cited alongside, same era.
A. Fallah, A. Mokhtari, and A. Ozdaglar, “On the convergence theory of gradient-based model-agnostic meta-learning algorithms,” in International Conference on Artificial Intelligence and Statistics . PMLR, 2020, pp. 1082–1092
2020
Cited alongside, same era.
2022
Closest in time.
C. favorita, “Corporación favorita grocery sales forecasting — kaggle,” https://www.kaggle.com/competitions/favorita-grocery-sales-forecasting , September 2022
2022
Closest in time.
A. A. Mir, M. Rafique, L. Hussain, A. S. Almasoud, M. Alajmi, F. N. Al-Wesabi, A. M. Hilal et al. , “An improved imputation method for accurate prediction of imputed dataset based radon time series,” Ieee Access , vol. 10, pp. 20 590–20 601, 2022
2022
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A. Jadon, “Regression Loss Functions Performance Evaluation in Time Series Forecasting using Temporal Fusion Transformers,” Sep. 2022, https://github.com/aryan-jadon/Regression-Loss-Functions-in-Time-Series-Forecasting-Tensorflow. [Online]. Available: https://doi.org/10.5281/zenodo.7126804
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2022
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Q. Wang, Y. Ma, K. Zhao, and Y. Tian, “A comprehensive survey of loss functions in machine learning,” Annals of Data Science , vol. 9, no. 2, pp. 187–212, 2022
2022
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