Problems of forecasting output
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Categoria dell'articolo: Research Article
Pubblicato online: 27 mar 2025
Ricevuto: 10 giu 2024
DOI: https://doi.org/10.2478/ijssis-2025-0006
Parole chiave
© 2025 Ali Sajae Mannaa et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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Analysis of the mathematical and statistical forecasting methods
| A time-series forecasting method based on weighing past observations with exponential attenuation. |
Easy to implement Takes into account recent observations |
It is sensitive to emissions/anomalies Does not take into account trends |
|
| A method based on the search for a linear relationship between independent and dependent variables. |
Easy to interpret Effective for linear dependencies |
Suitable only for linear dependencies Sensitive to emissions/anomalies |
|
| A method that allows us to model time series taking into account autoregression, moving average and seasonality. |
Takes into account the complex structure of time series Adapts to different types of data |
Requires defining model parameters Difficult to interpret |
|
| Using ML algorithms for forecasting based on historical data and external factors. |
Takes into account complex nonlinear dependencies Takes into account many input features |
Requires a large amount of data for training Requires a lot of computing resources |
|
| A method that extends exponential smoothing to account for seasonality and trend. |
It takes into account trends and seasonality Suitable for data with explicit cyclic behavior |
Requires parameter settings Strong dependence on initial conditions |
|
| A method based on the fact that objects with similar attributes have similar values of the target variable. |
Easy to implement Does not require assumptions about the data structure |
Sensitive to emissions Requires setting the k parameter |
|
| A method that reduces the dimensionality of data by projection onto a subspace with maximum variance. |
Effective for a large number of signs Reduces the effect of multicollinearity |
May lose its interpretability Does not take into account the dependencies between variables |
|
| A method developed by Facebook to predict time series based on seasonality, holidays and trends. |
Easy to use It takes into account seasonality and holidays |
It does not always show good results on short time series Does not take into account external factors |
|
| A method using ANNs for prediction based on learning from historical data. |
Takes into account complex nonlinear dependencies Works with different types of data |
Requires a large amount of data for training Difficult to set up and interpret |
|
| A method based on constructing an ensemble of decision trees and averaging their predictions. | Resistant to retraining and works with a large number of signs |
Prone to overtraining with suboptimal parameter settings Takes time to learn |
|
| Methods that combine several different forecasting methods to improve the accuracy of forecasts. |
Work with a variety of data characteristics Improve forecast accuracy |
Require additional configuration Difficult to implement |
|
| Methods that simulate random processes, including time series, using Gaussian distributions. |
Take into account uncertainty in forecasts Simulate nonlinear dependencies |
Require computing resources to evaluate Difficult to interpret |
|
| Methods based on Bayesian statistics for modeling and forecasting. |
Take into account the uncertainty in the forecasts Allow us to update forecasts based on new information |
Require the definition of Complex calculations |
|
| A method based on the construction of an ensemble of weak models, with each subsequent model correcting the errors of the previous one. |
High prediction accuracy Resistant to overtraining |
Demanding on resources Difficult to configure parameters |
|
| A method that uses RNNs with LSTM to analyze sequential data. |
Takes into account long-term dependencies Effective when working with sequential data |
Requires a large amount of data for training Requires computing resources |
|
| A method that models dependencies between variables in the form of a graph, where nodes represent variables and edges represent dependencies. |
Allows us to take into account the structure of dependencies between variables Works with different types of data |
Requires specification of the graph structure Difficult to interpret |
|
| A method that allows us to estimate not only the average value of the target variable but also its quantiles. |
Allows us to estimate the confidence intervals of forecasts Takes into account different levels of uncertainty |
Requires more data to accurately estimate quantiles High sensitivity to emissions/anomalies |
|
| A method based on the analysis of extreme (extreme) data values to predict rare events or extreme conditions. |
Effective in predicting rare events Used for risk assessment |
Requires a large amount of data on extreme values Difficult to interpret |
|
| A method that divides a time series into components (trend, seasonality, and residuals), and then predicts each component separately. |
Takes into account various characteristics of time series Effective in predicting nonstationary series |
Requires setting the parameters of the decomposition method Difficulties in analyzing the results |
|
| A method that combines graph models and neural networks for data structure analysis and forecasting. |
Takes into account complex dependencies between variables Works with graph data |
Requires a large amount of data for training Difficult to set up |
|
| A method using neural autoencoders to study the internal structure of time series and their subsequent prediction. |
Takes into account complex dependencies in the data Works with different types of time series |
Requires a lot of computing resources Requires a large amount of data for training |