Call/WhatsApp: +1 332 209 4094

Economics Demand Linear Programming

Description
For this assignment, you are requested to download the Forecast package in R. The package contains methods and tools for displaying and analyzing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling. Explore the gas (Australian monthly gas production) dataset in Forecast package by running the following commands in R

install.packages(“Forecast”)

library(forecast)

view(gas)

& do the following:

Read the data as a time series object in R. Plot the data (5 marks)
What do you observe? Which components of the time series are present in this dataset? (5 marks)
What is the periodicity of dataset? (5 marks)
Is the time series Stationary? Inspect visually as well as conduct an ADF test? Write down the null and alternate hypothesis for the stationarity test? De-seasonalise the series if seasonality is present? (20 marks)
Develop an ARIMA Model to forecast for next 12 periods. Use both manual and auto.arima (Show & explain all the steps) (20 marks)
Report the accuracy of the model (5 marks)
Please note the following:

Your submission should have two files –
1) Business report in PDF format with a word limit of 3000 words,
2) R Code file. Appendices are not counted in the word limit.
You must give the sources of data presented. Do not refer to blogs; Wikipedia etc.
Any assignment found copied/ plagiarized with other(s) will not be graded and marked as zero.
Please ensure timely submission as post deadline assignment will not be accepted.