Impact of Training and Testing Data Splits on Accuracy of Time Series Forecasting in Machine Learning

Medar, Ramesh; Rajpurohit, Vijay S.; Rashmi, B.

doi:10.1109/iccubea.2017.8463779

Cited by 31 publications

(19 citation statements)

References 1 publication

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“…The last line, the red one, is the predicted outcome from the data test Variation of cross-validation aims to find the best results from the training model. Based on [17] the bigger the training data, the better the model and the predictions, the better the results. However, the biggest size of training data does not mean it has the best results, so we vary the data to find better results.…”

Section: Resultsmentioning

confidence: 99%

Blood Glucose Prediction Using Convolutional Long Short-Term Memory Algorithms

Indrawan

Saadah

Yunanto

2021

khif

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Diabetes Mellitus is one of the preeminent causes of death to date. Effective procedures are necessary to prevent diabetes and avoid complications that may cause early death. A common approach is to control patient blood glucose, which necessitates a periodic measurement of blood glucose concentration. This study developed a blood glucose prediction system using a convolutional long short-term memory (Conv-LSTM) algorithm. Conv-LSTM is a variation of LSTM algorithms that are suitable for use in time series problems. Conv-LSTM overcomes the lack in the LSTM algorithm because the latter algorithm cannot access the content of previous memory cells when its output gate has closed. We tested the algorithm and varied the experiment to check the effect of the cross-validation ratio between 70:30 and 80:20. The study indicates that the cross-validation using a ratio of 70:30 data split is more stable compared to one with 80:20 data split. The best result shows a measure of 21.44 in RMSE and 8.73 in MAE. With the application of conv-LSTM using correct parameters and selected data split, our experiment attains accuracy comparable to the regular LSTM.

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Section: Resultsmentioning

confidence: 99%

Blood Glucose Prediction Using Convolutional Long Short-Term Memory Algorithms

Indrawan

Saadah

Yunanto

2021

khif

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“…To achieve the desired accuracy, it is essential to generate a reference for selecting the parameters that need to be recorded. The current study considers the use of different datasets as a useful method to ascertain the appropriate data that may have fewer variables and significant implications for predictions indeed [ 20 , 21 , 36 , 37 ]. So that the current study adopts the Spearman rank correlation coefficient approach in order to extract the best features, which is a commonly followed method to explore the relationships between attributes.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

Arulmozhi

Basak

Sihalath

et al. 2021

Animals

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Indoor air temperature (IAT) and indoor relative humidity (IRH) are the prominent microclimatic variables; still, potential contributors that influence the homeostasis of livestock animals reared in closed barns. Further, predicting IAT and IRH encourages farmers to think ahead actively and to prepare the optimum solutions. Therefore, the primary objective of the current literature is to build and investigate extensive performance analysis between popular ML models in practice used for IAT and IRH predictions. Meanwhile, multiple linear regression (MLR), multilayered perceptron (MLP), random forest regression (RFR), decision tree regression (DTR), and support vector regression (SVR) models were utilized for the prediction. This study used accessible factors such as external environmental data to simulate the models. In addition, three different input datasets named S1, S2, and S3 were used to assess the models. From the results, RFR models performed better results in both IAT (R2 = 0.9913; RMSE = 0.476; MAE = 0.3535) and IRH (R2 = 0.9594; RMSE = 2.429; MAE = 1.47) prediction among other models particularly with S3 input datasets. In addition, it has been proven that selecting the right features from the given input data builds supportive conditions under which the expected results are available. Overall, the current study demonstrates a better model among other models to predict IAT and IRH of a naturally ventilated swine building containing animals with fewer input attributes.

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“…Then, data miners harvest activity and engagement logs of students from the VLE and consequently producing training and testing datasets for the predictive model. The model will learn to perform a task using a training dataset, and the testing dataset will ensure that the model works correctly [44]. Moreover, the training dataset is used for model fitting or estimating the model's parameters.…”

Section: Fig 1 the Proposed Frameworkmentioning

confidence: 99%

A Hybrid Machine Learning Framework for Predicting Students’ Performance in Virtual Learning Environment

Evangelista

2021

Int. J. Emerg. Technol. Learn.

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Virtual Learning Environments (VLE), such as Moodle and Blackboard, store vast data to help identify students' performance and engagement. As a result, researchers have been focusing their efforts on assisting educational institutions in providing machine learning models to predict at-risk students and improve their performance. However, it requires an efficient approach to construct a model that can ultimately provide accurate predictions. Consequently, this study proposes a hybrid machine learning framework to predict students' performance using eight classification algorithms and three ensemble methods (Bagging, Boosting, Voting) to determine the best-performing predictive model. In addition, this study used filter-based and wrapper-based feature selection techniques to select the best features of the dataset related to students' performance. The obtained results reveal that the ensemble methods recorded higher predictive accuracy when compared to single classifiers. Furthermore, the accuracy of the models improved due to the feature selection techniques utilized in this study.

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Impact of Training and Testing Data Splits on Accuracy of Time Series Forecasting in Machine Learning

Cited by 31 publications

References 1 publication

Blood Glucose Prediction Using Convolutional Long Short-Term Memory Algorithms

Blood Glucose Prediction Using Convolutional Long Short-Term Memory Algorithms

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

A Hybrid Machine Learning Framework for Predicting Students’ Performance in Virtual Learning Environment

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