Figure 1:
Supervised learning algorithms used for big data analysis in selected articles.
Figure 2:
The results of forecasting the output of the 73 mm casing, tubing, and coupling using the SARIMAX model.
Table 1:
Analysis of the mathematical and statistical forecasting methods
| Name of the method | Brief description of the method | Advantages of the method | Disadvantages of the method |
|---|---|---|---|
| Exponential smoothing [7] | A time-series forecasting method based on weighing past observations with exponential attenuation. |
|
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| Linear regression [8] | A method based on the search for a linear relationship between independent and dependent variables. |
|
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| ARIMA [9] | A method that allows us to model time series taking into account autoregression, moving average and seasonality. |
|
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| Forecasting based on ML [10] | Using ML algorithms for forecasting based on historical data and external factors. |
|
|
| Holt-Winters method [11] | A method that extends exponential smoothing to account for seasonality and trend. |
|
|
| Prediction by the k-nearest neighbor method [12] | A method based on the fact that objects with similar attributes have similar values of the target variable. |
|
|
| Principal component method [13] | A method that reduces the dimensionality of data by projection onto a subspace with maximum variance. |
|
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| Facebook prophet [8] | A method developed by Facebook to predict time series based on seasonality, holidays and trends. |
|
|
| Neural network method [14] | A method using ANNs for prediction based on learning from historical data. |
|
|
| Random forest method [15] | 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 |
|
| Time-series method SARIMA [16] | |||
| Hybrid models [17] | Methods that combine several different forecasting methods to improve the accuracy of forecasts. |
|
|
| Gaussian processes [18] | Methods that simulate random processes, including time series, using Gaussian distributions. |
|
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| Bayesian methods [19] | Methods based on Bayesian statistics for modeling and forecasting. |
|
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| Gradient boosting [20] | A method based on the construction of an ensemble of weak models, with each subsequent model correcting the errors of the previous one. |
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| LSTM [21] | A method that uses RNNs with LSTM to analyze sequential data. |
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| Method of graphical models [22] | A method that models dependencies between variables in the form of a graph, where nodes represent variables and edges represent dependencies. |
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| Quantile regression [23] | A method that allows us to estimate not only the average value of the target variable but also its quantiles. |
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| Method of extreme cases [24] | A method based on the analysis of extreme (extreme) data values to predict rare events or extreme conditions. |
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| Time-series decomposition method [25] | A method that divides a time series into components (trend, seasonality, and residuals), and then predicts each component separately. |
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| Graph neural networks [26] | A method that combines graph models and neural networks for data structure analysis and forecasting. |
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| Temporary neural autoencoder [27] | A method using neural autoencoders to study the internal structure of time series and their subsequent prediction. |
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Figure 3:
Results of forecasting the output of the casing coupling using the SARIMAX model.