Time Series Forecasting in Turning Processes Using ARIMA Model

Jimenez-Cortadi, Alberto; Boto, Fernando; Irigoien, Itziar; Sierra, Basilio; Rodriguez, German

doi:10.1007/978-3-319-99626-4_14

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…end for (20) Train the Rotation Forest in training set (21) for training set do (22) y � D (k) (F (k) ) (23) end for (24) Calculate y〈k〉 (25) for training and testing set do (26) y � D (k) (F (k) ) (27) end for (28) Calculate changes (29) s � MSE(y (k) , y (k) ) (30) until k > k m ax or s < eps (31) Construct F<k> (32) for j from 1 to k do (33) Calculate e (k) , F (k) , y (k) (34) end for (35)…”

Section: Discussion About Ma Termsmentioning

confidence: 99%

“…Derived from this combination, the differential process is further added to it, giving rise to the autoregressive integrated moving average (ARIMA). Researchers solve forecasting tasks with all types of ARIMA models since the time they are invented [22][23][24]. e long-range dependence expressed in the MA terms of these models is critical in the prognostics of bearings [25].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Recursive ARIMA Method with Recurrent Process for Remaining Useful Life Estimation of Bearings

Luo

Wang

Yang

2022

Shock and Vibration

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A typical way to predict the remaining useful life (RUL) of bearings is to predict certain health indicators (HIs) according to the historical HI series and forecast the end of life (EOL). The autoregressive neural network (ARNN) is an early idea to combine the artificial neural network (ANN) and the autoregressive (AR) model for forecasting, but the model is limited to linear terms. To overcome the limitation, this paper proposes an improved autoregressive integrated moving average with the recurrent process (ARIMA-R) method. The proposed method adds moving average (MA) components to the framework of ARNN, adding the long-range dependence and nonlinear factors. To deal with the recursive characteristics of the MA term, a process of MA component estimating is constructed based on the expectation-maximum method. In the concrete realization of the method, the rotation tree (RTF) is introduced in place of ANN to improve the prediction performance. The experiment on FEMTO datasets reveals that the proposed ARIMA-R method outperforms the ARNN method in terms of predictive performance evaluation indicators.

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Section: Discussion About Ma Termsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Recursive ARIMA Method with Recurrent Process for Remaining Useful Life Estimation of Bearings

Luo

Wang

Yang

2022

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…Jimenez-Cortadi et al [13] introduced a Spatio-Temporal Graph Neural Networks for Traffic Flow Forecasting. This paper presents a spatio-temporal graph neural network model for traffic flow forecasting.…”

Section: Arima and Sarima Modelsmentioning

confidence: 99%

Time Series Modeling and Forecasting Using Autoregressive Integrated Moving Average and Seasonal Autoregressive Integrated Moving Average Models

Arumugam,

Natarajan

2023

I2M

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Time series analysis is pivotal in discerning temporospatial data patterns and facilitating precise forecasts. This study scrutinizes the cardinal challenges associated with time series modeling, namely stationarity, parsimony, and overfitting, focusing on the application of Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. An examination of six datasets reveals that these models adeptly encapsulate underlying data trends, enabling reliable predictions and yielding insightful conclusions. Relative to baseline methods, the proposed models demonstrate superior performance, as indicated by five evaluation metrics: Mean Squared Error (MSE), Frantic, Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Theil's U-statistics. The most parsimonious ARIMA or SARIMA model was selected for each dataset, with the resultant forecast summary graphically demonstrating the proximity between original and predicted observations. This study aims to contribute to the discourse on the validity and applicability of ARIMA and SARIMA models in time series analysis and forecasting.

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Deep Neural Network for Multivariate Time-Series Forecasting

Bhanja¹,

Das

2020

Advances in Intelligent Systems and Computing

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Time Series Forecasting in Turning Processes Using ARIMA Model

Cited by 4 publications

References 12 publications

An Improved Recursive ARIMA Method with Recurrent Process for Remaining Useful Life Estimation of Bearings

An Improved Recursive ARIMA Method with Recurrent Process for Remaining Useful Life Estimation of Bearings

Time Series Modeling and Forecasting Using Autoregressive Integrated Moving Average and Seasonal Autoregressive Integrated Moving Average Models

Deep Neural Network for Multivariate Time-Series Forecasting

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