Comparison of Forecasting Performance of ARIMA LSTM  and HYBRID Models for The Sales Volume Budget of a Manufacturing Enterprise

Temür, Ayşe Soy; Yıldız, Şule

doi:10.26650/ibr.2021.51.0117

Cited by 10 publications

(13 citation statements)

References 35 publications

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“…MAE quantifies the average magnitude of errors between predicted and actual values. It offers an insight into the accuracy of the model, focusing on the size of the errors without taking into account the direction of these errors [54]. MSE computes the average of the squares of the errors.…”

Section: Results Imputationmentioning

confidence: 99%

Imputation of missing microclimate data of coffee-pine agroforestry with machine learning

Nurwarsito,

Suprayogo,

Sakti

et al. 2024

Int. J. Adv. Intell. Informatics

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This research presents a comprehensive analysis of various imputation methods for addressing missing microclimate data in the context of coffee-pine agroforestry land in UB Forest. Utilizing Big data and Machine learning methods, the research evaluates the effectiveness of imputation missing microclimate data with Interpolation, Shifted Interpolation, K-Nearest Neighbors (KNN), and Linear Regression methods across multiple time frames - 6 hours, daily, weekly, and monthly. The performance of these methods is meticulously assessed using four key evaluation metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results indicate that Linear Regression consistently outperforms other methods across all time frames, demonstrating the lowest error rates in terms of MAE, MSE, RMSE, and MAPE. This finding underscores the robustness and precision of Linear Regression in handling the variability inherent in microclimate data within agroforestry systems. The research highlights the critical role of accurate data imputation in agroforestry research and points towards the potential of machine learning techniques in advancing environmental data analysis. The insights gained from this research contribute significantly to the field of environmental science, offering a reliable methodological approach for enhancing the accuracy of microclimate models in agroforestry, thereby facilitating informed decision-making for sustainable ecosystem management.

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Section: Results Imputationmentioning

confidence: 99%

Imputation of missing microclimate data of coffee-pine agroforestry with machine learning

Nurwarsito,

Suprayogo,

Sakti

et al. 2024

Int. J. Adv. Intell. Informatics

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“…The obtained result with hybrid models were individually compared, it was observed that they could reduce the general variance or error, even if they are unrelated. Due to this reason, hybrid models are recognized as the most successful models for forecasting tasks [15]. This information will support the decision makers for energy management and decarbonization planning.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, it has become evident that hybrid methods yielded better results compared to a single method. A summary of studies that applied hybrid model for accurate prediction: Soy Temür et al [15] proposed a hybrid model which consists of a combination of the linear model (ARIMA), nonlinear model (LSTM), and hybrid (LSTM and ARIMA) model to improve system performance compared to a single model. A prediction method (GA-CNN-LSTM) combines a convolutional neural network (CNN) and a long-short-term memory network (LSTM) and is optimized by a genetic algorithm (GA) [16].…”

Section: Related Work On Arima-lstmmentioning

confidence: 99%

“…The LSTM architecture consists of a set of recurrently connected sub-networks, known as memory blocks, the idea behind the memory block is to maintain its state over time and regulate the information flow through non-linear gating units [28]. Figure 1 is the N architecture of LSTM adopted from the work of [15]. Figure 2 depicted LSTM Structure.…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

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Application of ARIMA-LSTM for Manufacturing Decarbonization Using 4IR Concepts

Adenuga

Mpofu²,

Modise³

2022

Lecture Notes in Mechanical Engineering

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Increasing climate change concerns call for the manufacturing sector to decarbonize its process by introducing a mitigation strategy. Energy efficiency concepts within the manufacturing process value chain are proportional to the emission reductions, prompting decision makers to require predictive tools to execute decarbonization solutions. Accurate forecasting requires techniques with a strong capability for predicting automotive component manufacturing energy consumption and carbon emission data. In this paper we introduce a hybrid autoregressive moving average (ARIMA)-long short-term memory network (LSTM) model for energy consumption forecasting and prediction of carbon emission within the manufacturing facility using the 4IR concept. The method could capture linear features (ARIMA) and LSTM captures the long dependencies in the data from the nonlinear time series data patterns, Root means square error (RMSE) is used for data analysis comparing the performance of ARIMA which is 448.89 as a single model with ARIMA-LSTM hybrid model as actual (trained) and predicted (test) 59.52 and 58.41 respectively. The results depicted RMSE values of ARIMA-LSTM being extremely smaller than ARIMA, which proves that hybrid ARIMA-LSTM is more suitable for prediction than ARIMA.

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“…It is mainly utilized in deep learning that performs better on substantial datasets. The outcome of this test is that they can repeat their achievement with a 13 percent -16 percentage error margin [13]. LSTMs are a form of RNN that focuses on learning longterm dependency.…”

Section: Literature Reviewmentioning

confidence: 99%

Sales Prediction of Cardiac Products by Time Series and Deep Learning

Arshad

Tahir

2022

IJIST

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Maintaining inventory level to avoid high inventory costs is an issue for Cardiac Product Distribution Companies (CPDCs) because of the shortage of their products which affect their sale and causes loss of the customer. This research aims to provide a method for predicting the upcoming demand of the Balloon and Stents by using time series analysis (Auto Regression Integrated Moving Average) and Deep learning (Long-Short Term Memory). To conduct this research, data was collected from Pakistan’s leading cardiac product distributors to determine the method's performance. The findings were compared using Mean absolute error (MAE) and Root Mean Square Error (RMSE). Result conclude that the ARIMA algorithm successfully forecasts cardiac products sale.

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Comparison of Forecasting Performance of ARIMA LSTM and HYBRID Models for The Sales Volume Budget of a Manufacturing Enterprise

Cited by 10 publications

References 35 publications

Imputation of missing microclimate data of coffee-pine agroforestry with machine learning

Imputation of missing microclimate data of coffee-pine agroforestry with machine learning

Application of ARIMA-LSTM for Manufacturing Decarbonization Using 4IR Concepts

Sales Prediction of Cardiac Products by Time Series and Deep Learning

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