ORBITS

With the emergence of the Internet of Things (IoT), time series streams have become ubiquitous in our daily life. Recording such data is rarely a perfect process, as sensor failures frequently occur, yielding occasional blocks of data that go missing in multiple time series. These missing blocks do not only affect real-time monitoring but also compromise the quality of online data analyses. Effective streaming recovery (imputation) techniques either have a quadratic runtime complexity, which is infeasible for any moderately sized data, or cannot recover more than one time series at a time. In this paper, we introduce a new online recovery technique to recover multiple time series streams in linear time. Our recovery technique implements a novel incremental version of the Centroid Decomposition technique and reduces its complexity from quadratic to linear. Using this incremental technique, missing blocks are efficiently recovered in a continuous manner based on previous recoveries. We formally prove the correctness of our new incremental computation, which yields an accurate recovery. Our experimental results on real-world time series show that our recovery technique is, on average, 30% more accurate than the state of the art while being vastly more efficient.

IMPLEMENTASI METODE CENTROID DECOMPOSITION UNTUK PERAMALAN PADA DATA CUACA

Data mining adalah sebuah fase pencarian pengetahuan pada kumpulan suatu data. Data mining juga adalah sebuah proses ekstraksi dari informasi-informasi dan pengetahuan-pengetahuan yang berguna yang didapat dari kumpulan data yang besar, tidak lengkap, acak, dan ambigu. Berdasarkan pengetahuan tersebut, penelitian ini dilakukan untuk mengetahui apakah metode yang diterapkan oleh peneliti sebelumnya pada penanganan missing values dapat diterapkan pada proses prediksi dengan beberapa penyesuaian. Seiring bertambahnya titik prediksi, hasil dari metode Ekstrapolasi Linear semakin buruk. Dengan kata lain tidak cocok untuk melakukan prediksi jangka menengah hingga panjang, namun dapat dilakukan menggunakan metode Centroid Decomposition.