محلی‌سازی مدل‌های پیش‌بینی سراسری با یک رویکرد خوشه‌بندی | ||
| مدیریت مهندسی و رایانش نرم | ||
| دوره 11، شماره 1 - شماره پیاپی 20، مرداد 1404، صفحه 155-134 اصل مقاله (1.29 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22091/jemsc.2025.11595.1218 | ||
| نویسندگان | ||
| حسین عباسی مهر؛ محمد خودی زاده نهاری* | ||
| دانشکده فناوری اطلاعات و مهندسی کامپیوتر، دانشگاه شهید مدنی آذربایجان، تبریز، ایران | ||
| چکیده | ||
| با تولید روز افزون داده‌های سری زمانی، مدل‌های پیش‌بینی که بر روی مجموعه‌ای از سری‌های زمانی آموزش داده می‌شوند و به‌عنوان مدل‌های پیش‌بینی سراسری شناخته می‌شوند، عملکرد بهتری نسبت به مدل‌های پیش‌بینی تک‌متغیره که بر روی سری‌های جداگانه آموزش می‌بینند، دارند. با این حال، عملکرد مدل‌های سراسری ممکن است در مواجهه با مجموعه داده‌های ناهمگن سری‌های زمانی با طول‌های متفاوت کاهش یابد. در این مطالعه، یک روش جدید برای محلی‌سازی مدل‌های پیش‌بینی سراسری مبتنی بر خوشه‌بندی ارائه می‌شود. مراحل اصلی روش ارائه شده شامل (1) استخراج ویژگی‌های مرتبط از هر سری زمانی (2) خوشه‌بندی سری‌های زمانی بر پایه ویژگی‌های استخراج شده با استفاده از الگوریتم‌های K-Medoids و خوشه‌بندی طیفی (3) پیاده‌سازی یک مدل پیش‌بینی سراسری با استفاده ازمدل مبتنی بر شبکه کانولوشنی زمانی و آموزش آن برای هر خوشه است. برای ارزیابی دقت پیش‌بینی، آزمایشاتی بر روی مجموعه داده M3 شامل 1426 سری زمانی با طول‌های غیریکسان انجام شد. نتایج آزمایشات نشان‌دهنده عملکرد برتر مدل‌های پیشنهادی در مقایسه با مدل‌های پایه و مدل‌های محک است. در معیار SMAPE، مدل پیشنهادی 0.57 خطای کمتری دارد. | ||
| کلیدواژه‌ها | ||
| پیش‌بینی سری زمانی؛ مدل پیش‌بینی سراسری؛ خوشه‌بندی‌سری زمانی؛ شبکه حافظه کوتاه‌مدت طولانی | ||
| عنوان مقاله [English] | ||
| Localization of Global Forecasting Models with a Clustering Approach | ||
| نویسندگان [English] | ||
| Hosein Abbasimehr؛ Mohammad Khodizadeh-Nahari | ||
| Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran | ||
| چکیده [English] | ||
| With the increasing generation of time series data, forecasting models trained on a set of time series, known as global forecasting models, outperform univariate forecasting models trained on individual series. However, the performance of global models may decrease when faced with heterogeneous data sets of time series with different lengths. In this study, a new method for localization of clustering-based global forecasting models is presented. The main steps of the proposed method include (1) extracting relevant features from each time series (2) clustering time series based on features extracted using K-Medoids and spectral clustering algorithms (3) implementing a global forecasting model using a combination of Temporal Convolution Network and its training for each cluster. To evaluate the prediction accuracy of the proposed approach, experiments were conducted on the M3 dataset that contains 1426 time series with unequal-length. The results of the experiments show the superior performance of the proposed clustering-based models compared to the baseline models and the benchmark models. The proposed model has 0.57 less error in terms of SMAPE metric. | ||
| کلیدواژه‌ها [English] | ||
| Time series forecasting, Global forecasting model, Time series clustering, Long short term memory network | ||
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