Time series analysis is a powerful statistical tool for examining data collected over time to identify patterns, trends, and seasonal variations. It empowers researchers, policymakers, and businesses to understand dynamic changes and make accurate forecasts for effective planning and decision-making. In public health, time series analysis helps track and predict infectious disease outbreaks, while in meteorology, it supports weather forecasting and disaster management. In economics and business, models like ARIMA and ETS are used for forecasting, risk assessment, and policy planning based on trends in inflation and employment. This webinar demonstrates the use of R for time series analysis, including the forecast package, autoplot() visualization, smoothing techniques (Holt, Brown, Winters), and models like ARIMA, SARIMA, and ETS. It further discusses statistical testing for stationarity, linearity, and seasonality, along with model comparison and accuracy evaluation using real-world data.
Zoom link: https://uoc-gr.zoom.us/j/88659969718?pwd=g6bjYPDCuUQo1bzVxjjbgQL4xFN1f3.1
