Lecture
Time Series Analysis: ARIMA and Seasonal Models
In course
DEMO: tempor exercitation ea culpa
Duis nostrud minim mollit nostrud. Tempor nostrud eu irure pariatur nulla aliqua reprehenderit ex in culpa duis deserunt. Consequat labore veniam qui velit ea nisi consequat laboris aute adipisicing voluptate cillum. Esse eiusmod nisi elit adipisicing incididunt dolor pariatur occaecat nulla officia qui. Commodo exercitation voluptate eu eu reprehenderit amet veniam commodo dolore qui fugiat velit id sit. In consectetur magna exercitation excepteur ipsum esse ipsum culpa proident ad amet adipisicing.
Description
This lecture covers the ARIMA(1,1,0) model for determining second-order properties of a process, the ARI(1,1) process, and seasonal models for econometric and financial processes. It explains the Box-Jenkins methodology for model building and identification using time series plots, ACF, and PACF.
Instructor
anim minim
Excepteur anim laborum pariatur reprehenderit amet laborum ut pariatur eu aliqua enim ex. Exercitation officia reprehenderit enim nulla voluptate dolor commodo laboris. Magna aliqua voluptate sunt mollit minim dolor voluptate adipisicing duis sint.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related lectures (32)
Time Series: Parametric Estimation
Covers parametric estimation, seasonal modeling, Box-Jenkins methods, variance calculations, and dependence measures in time series analysis.
Integrated and Seasonal Processes: Time Series
Explores parametric estimation, integrated processes, seasonal modeling, and ARIMA model building in time series analysis.
Time Series: Autoregressive Models
Explores autoregressive models for time series analysis, covering AR(1), AR(2), identification, and MA models.
Time Series Models: Autoregressive Processes
Explores time series models, emphasizing autoregressive processes, including white noise, AR(1), and MA(1), among others.
Parametric Signal Models: Matlab Practice
Covers parametric signal models and practical Matlab applications for Markov chains and AutoRegressive processes.