Kurikulum 2024

Kode Mata KuliahAK2283 / 3 SKS
Penyelenggara108 - Aktuaria / FMIPA
KategoriKuliah
Bahasa IndonesiaEnglish
Nama Mata KuliahAnalisis Deret WaktuTime Series Analysis
Bahan Kajian
  1. Pengenalan model deret waktu dan model regeresi linier
  2. Kestasioneran
  3. Model-model stasioner
  4. Model-model tak stasioner
  5. Identifikasi model
  6. Estimasi parameter menggunakan metode momen dan kuadrat terkecil
  7. Estimasi parameter menggunakan metode likelihood maksimum
  8. Uji diagnostik menggunakan analisis residual
  9. Prakiraan (forecasting) model ARIMA
  10. Batas prediksi prakiraan model ARIMA
  11. Model musiman (seasonal)
  12. Model deret waktu heteroscedasticity ARCH(1) dan GARCH(1) (pengayaan)
  13. Estimasi likelihood maksimum dan uji diagnostik model deret waktu heteroscedasticity (pengayaan)
  1. Introduction to time series models and linear regression models
  2. Stationarity
  3. Stationary models
  4. Non-stationary models
  5. Model identification
  6. Parameter estimation uses the moment method and least squares
  7. Parameter estimation uses the maximum likelihood method
  8. Diagnostic tests use residual analysis
  9. Forecast (forecasting) ARIMA models
  10. Prediction limits of ARIMA model forecasts
  11. Seasonal model (seasonal)
  12. Time series model heteroscedasticity ARCH(1) and GARCH(1) (enrichment)
  13. Maximum likelihood estimation and diagnostic testing of time series modelsheteroscedasticity(enrichment)
Capaian Pembelajaran Mata Kuliah (CPMK)
  1. Memiliki pengetahuan dan wawasan yang cukup tentang konsep deret waktu dan asumsi kestasioneran.
  2. Dapat mengidentifikasi model deret waktu stasioner dan non stasioner.
  3. Mampu menyelesaikan masalah berkaitan dengan penerapan model deret waktu ARIMA.
  4. Dapat menginterpretasikan prakiraan (forecasting) hasil model deret waktu.
  1. Have sufficient knowledge and insight into the concept of time series and stationarity assumptions.
  2. Can identify stationary and non-stationary time series models.
  3. Able to solve problems related to the application of ARIMA time series models.
  4. Can interpret forecasts (forecasting) time series model results.
Metode PembelajaranCeramah dan diskusiLectures and discussions
Modalitas PembelajaranBauran, Sinkron/asinkron, dan Mandiri/ KelompokMixed, Synchronous/asynchronous, and Independent/Group
Jenis NilaiABCDE
Metode PenilaianUTS, UAS, Kuis, Tugas & Praktikum.Exam, Quiz, Assignment and Lab
Catatan TambahanTidak adaNA