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د. مها أحمد حمزة عمير Dr. Maha Ahmad Omair

Assistant Professor

عضو هيئة تدريس - قسم الإحصاء وبحوث العمليات

كلية العلوم
كلية العلوم - المدينة الجامعية للطالبات (مبنى 5 مكتب رقم 347)
course

Time Series and Forecasting (STAT-436)

List of Topics
Introduction: Definitions and Examples.
trend – seasonality – cyclical
Transformation:  Differences method – Seasonal adjustment.
Forecasting: How to forecast future - adequacy of a forecast - regression forecasting against time series forecasting
Some adequacy measures (MAD, MSE, MAPE).
Decomposition and smoothing of times series: moving averages - exponential smoothing double exponential smoothing.
Stationary Time Series Models: Auto-Regressive processes (AR(1), AR(2), AR(p)), Moving Average processes (MA(1), MA(2), MA(q)), The mixed Autoregressive-Moving Average Model ARMA(p,q).
Forecasting: Minimum Mean Square Error Forecasts for ARMA and ARIMA models.
Forecasting, prediction limits and updating forecasts.
ARIMA(p,d,q) models:  Autocorrelation and partial autocorrelation functions - identification of appropriate model
Fitting models to real and simulated data sets. Diagnostic checks on the residuals.
Case studies: training on how to analyze real life data sets using the statistical package MINITAB - write reports.
course attachements