Combining Forecasts of Arima and Exponential Smoothing Models

Attanayake, A. M. C. H.; Perera, S. S. N.; Liyanage, Uditha Prabhath

doi:10.17654/as059020199

Cited by 3 publications

(3 citation statements)

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“…Teknik ini dapat menghilangkan perubahan yang tidak disengaja dalam deret waktu, memperlihatkan metode dengan tren data terbaru, dan bekerja secara akurat dalam memahami prediksi jangka pendek. Metode exponential smoothing merupakan sebuah metode yang cocok untuk digunakan disaat tidak ada tren atau pola musiman [13]. Penelitian ini menggunakan metode Exponential Smoothing, dimana menjelaskan bahwa data mengalami ketakstabilan di sekitar nilai rata-rata yang stabil.…”

Section: Metode Exponential Smoothingunclassified

Prediksi Jumlah Penerimaan Mahasiswa Baru Dengan Metode Linear Regression dan Exponential Smoothing

Luthfi Mawardi,

Umar Alfaruq,

Gandung Triyono

2024

ijcs

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Penerimaan mahasiswa baru adalah elemen kunci dalam manajemen perguruan tinggi yang memerlukan perencanaan yang cermat serta kebijakan dan strategi untuk mengambil keputusan kegiatan belajar mengajar. Untuk merencanakan prediksi jumlah penerimaan mahasiswa baru yang akurat adalah penting dalam pengambilan keputusan terkait alokasi sumber daya, perencanaan fasilitas, dan program akademik. Oleh karena itu, tujuan dari penelitian ini adalah untuk memprediksi jumlah mahasiswa baru di Fakultas Teknologi Industri dan Informatika UHAMKA menggunakan metode Linier Regression dan Exponential Smoothing. Metode ini digunakan untuk menangani fluktuasi dalam data historis yang mungkin disebabkan oleh perubahan demografi, persaingan antar institusi, dan faktor ekonomi lainnya. Penelitian Ini menggunakan metode data mining untuk melakukan prediksi jumlah penerimaan mahaiswa baru pada Program Studi Teknik Informatika Fakultas Teknologi Industri dan Informatika UHAMKA. Sumber data penelitian ini adalah jumlah calon mahasiswa baru di 15 Tahun ke belakang yaitu data 2008 sampai 2023. dengan menggunakan 2 metode yaitu linear regression dan exponential smoothing. Prediksi jumlah penerimaan mahasiswa baru menunjukkan nilai prediksi yang sangat baik yaitu antara 0-10%. Nilai MAPE pada prediksi adalah 0,99% dengan Regresi Linear dan 5,07% ketika menggunakan Exponential Smoothing. Dalam pengujian bahwa Linear Regression menghasilkan nilai MAPE lebih kecil daripada metode Exponential Smoothing, yang berarti metode hasil prediksinya lebih mendekati dengan jumlah penerimaan mahasiswa baru yang sebenarnya.

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Section: Metode Exponential Smoothingunclassified

Prediksi Jumlah Penerimaan Mahasiswa Baru Dengan Metode Linear Regression dan Exponential Smoothing

Luthfi Mawardi,

Umar Alfaruq,

Gandung Triyono

2024

ijcs

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“…Overall, the data-driven methods for traffic forecast can be classified into two categories: parametric approach and non-parametric approach ( 1 ). The parametric approach deals with the relationship between travel time and other traffic variables, including historical average ( 2, 3 ), linear regression ( 4–6 ), auto regressive integrated moving average (ARIMA) ( 6–8 ), and Kalman filter ( 9–11 ). Non-parametric approach includes neural networks (NNs) ( 12–14 ), k -nearest neighborhood ( k -NN) method ( 15–17 ), and support vector machine ( 18–20 ).…”

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confidence: 99%

Short-Term Travel-Time Prediction using Support Vector Machine and Nearest Neighbor Method

Meng

Toan

Wong

et al. 2022

Transportation Research Record: Journal of the Transportation R

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This paper presents an investigation into the performance of support vector machine (SVM) in short-term travel-time prediction in comparison with baseline methods, including the historical mean, current time based, and time varying coefficient predictors. To demonstrate the SVM performance, 1-month time-series speed data on a section of Pan-Island Expressway in Singapore were used to estimate the travel time for training and testing the SVM model. The results show that the SVM method significantly outperforms the baseline methods in both normal and recurring congestion over a wide range of prediction intervals. In studying SVM prediction behavior under incident situations, the results show that all the predictors are not responsive enough using 15-minute aggregated field data, but the SVM predicted outcome follows the test data profile closely for 2-minute aggregated simulated data. Finally, to improve the prediction performance, an empirical k-nearest neighbor method is introduced to retrieve patterns closest to the test vector for SVM training. The results show that k-Nearest Neighbor is an attractive tool for SVM travel-time prediction. In retrieving the most similar patterns for SVM training, k-nearest neighbor allows dramatic reduction of training size to accelerate the training task while maintaining prediction accuracy.

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“…Traffic flow prediction approaches so far can be broadly classified into the time-series analysis approach and the machine learning approach ( 1 ). Techniques associated with the time-series analysis approach cover a wide range, from historical average ( 2 ) to Kalman Filtering ( 3 – 6 ), Exponential Smoothing ( 7 , 8 ), and Autoregressive Moving Average (ARIMA) models ( 7 , 9 – 11 ). Techniques associated with the machine learning approach include k-Nearest Neighbor ( 12 – 15 ), Artiﬁcial Neural Network ( 16 – 18 ), and Support Vector Machine (SVM) ( 19 – 21 ).…”

mentioning

confidence: 99%

Support Vector Machine for Short-Term Traffic Flow Prediction and Improvement of Its Model Training using Nearest Neighbor Approach

Toan

Truong

2020

Transportation Research Record: Journal of the Transportation R

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Short-term prediction of traffic flow is essential for the deployment of intelligent transportation systems. In this paper we present an efficient method for short-term traffic flow prediction using a Support Vector Machine (SVM) in comparison with baseline methods, including the historical average, the Current Time Based, and the Double Exponential Smoothing predictors. To demonstrate the efficiency and accuracy of the SVM method, we used one-month time-series traffic flow data on a segment of the Pan Island Expressway in Singapore for training and testing the model. The results show that the SVM method significantly outperforms the baseline methods for most prediction intervals, and under various traffic conditions, for the rolling horizon of 30 min. In investigating the effect of the input-data dimension on prediction accuracy, we found that the rolling horizon has a clear effect on the SVM’s prediction accuracy: for the rolling horizon of 30–60 min, the longer the rolling horizon, the more accurate the SVM prediction is. To look for a solution for improvement of the SVM’s training performance, we investigate the application of k-Nearest Neighbor method for SVM training using both actual data and simulated incident data. The results show that the k- Nearest Neighbor method facilitates a substantial reduction of SVM training size to accelerate the training without compromising predictive performance.

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Combining Forecasts of Arima and Exponential Smoothing Models

Cited by 3 publications

References 0 publications

Prediksi Jumlah Penerimaan Mahasiswa Baru Dengan Metode Linear Regression dan Exponential Smoothing

Prediksi Jumlah Penerimaan Mahasiswa Baru Dengan Metode Linear Regression dan Exponential Smoothing

Short-Term Travel-Time Prediction using Support Vector Machine and Nearest Neighbor Method

Support Vector Machine for Short-Term Traffic Flow Prediction and Improvement of Its Model Training using Nearest Neighbor Approach

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