Zamelina, Armando Jacquis Federal and Dr. Suci Astutik, S.Si., M.Si. and Rahma Fitriani, S.Si., M.Sc., Ph.D. (2024) Enhancing Lagrange Weighted Fuzzy Time Series Forecasting Through Particle Swarm Optimization. Magister thesis, Universitas Brawijaya.
Abstract
Peramalan deret waktu adalah salah satu teknik penting yang membantu pengambilan keputusan strategis dan memitigasi potensi risiko –Salah satunya adalah Weighted fuzzy time series (WFTS). Selain itu, panjang interval WFTS adalah penting dalam pemodelan dan keakuratannya model tersebut dalam memprediksi nilai masa depan. Oleh karena itu, penelitian ini menerapkan dua optimasi pada WFTS, yaitu (1) mencari panjang interval optimum WFTS melalui Particle Swarm Optimization dan (2) Lagrange Quadratic untuk mengoptimalkan bobot pada interval fuzzy. Dalam penelitian ini, digunakan model peramalan suhu udara rata-rata univariat yang berlokasi di kota Malang. Penelitian ini terutama bertujuan untuk memperoleh panjang interval optimal pada peramalan deret waktu fuzzy, yaitu meningkatkan akurasinya dengan mencari panjang interval optimal. Berdasarkan hasil, model optimasi ganda yang diusulkan melebihi WFTS klasik dalam peramalan. Model yang diusulkan unggul berdasarkan nilai metrik evaluasi. Telah diketahui juga bahwa penerapan PSO untuk menemukan panjang interval optimal telah meningkatkan akurasi WFTS klasik. WFTS klasik memiliki MAPE dan RMSE masing-masing sebesar 2,25 dan 0,71, sedangkan model optimalisasi ganda yang diusulkan memiliki 1,59 dan 0,49. Pendekatan ini mengidentifikasi nilai interval terbaik dan memberikan bobot optimal terkait setiap titik data, sehingga memberikan wawasan yang kuat untuk perkiraan Suhu Udara.
English Abstract
Time series Forecasting is one of crucial techniques that helps with strategic decisionmaking and mitigating potential risks –One of which is Weighted fuzzy time series (WFTS). Besides, the interval length of the WFTS plays a crucial role in its modelization and accuracy in predicting future values. Therefore, this research implements a dual optimization on WFTS, which are (1) Particle Swarm Optimization to find the optimum interval length of the WFTS and (2) a Lagrange quadratic to optimize the weight of the fuzzy interval. In this research, a univariate Average Air Temperature located in Malang is used to perform forecasting model. This research mainly aims to acquire an optimized interval length on fuzzy time series forecasting, i.e., improving its accuracy by finding the optimal interval length. Based on the result, the proposed dual optimization model outperforms the classical WFTS on forecasting. The proposed model excels based on the evaluation metrics values. It has been noticed also that implementing PSO to find the optimum interval length has improved the accuracy of the classical WFTS. The classical WFTS has MAPE and RMSE of 2.25 and 0.71, respectively, while the proposed dual optimized model has 1.59 and 0.49. This approach identifies the best interval values and provides optimum weights related to each data point, providing solid insights for Air Temperature forecasting.
| Item Type: | Thesis (Magister) |
|---|---|
| Identification Number: | 0424090027 |
| Uncontrolled Keywords: | Peramalan; Weighted fuzzy time series; Particle Swarm Optimization; Lagrange Quadratic |
| Divisions: | S2/S3 > Magister Statistika, Fakultas MIPA |
| Depositing User: | Unnamed user with username nova |
| Date Deposited: | 11 Sep 2024 07:32 |
| Last Modified: | 11 Sep 2024 07:32 |
| URI: | http://repository.ub.ac.id/id/eprint/227453 |
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Text (DALAM MASA EMBARGO)
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