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README.md

Parametric and Non-parametric Models in Forecasting

This is an adaptation of a view over forecasting time series with parametric and non-parametric models through number of interactive plots, with data supplied by R through Shiny in real- time.

Overview

The aim of this project is to compare performance of parametric and non-parametric time series trend models, by means of simulation. The models to be compared include simple functions such as polynomials and sinusoidal functions, as well as a real dataset example: London mortality rates for years 1st Jan 2000- 31st Dec 2005.

Method used to estimate the trends and make predictions on the generated time series data was Integrated Nested Laplace Approximation (INLA) implemented through R-INLA program package, available here.

Resources

Live application hosted at Glimmer (IE does not correctly render IFRAME).

The summary in the form of a report is available for download here.

Snapshot in the form of poster is avaiable at here.

For general Shiny instuctions, please refer to R-Studio tutorial.

My other projects and WIP.