package contains methods and tools for displaying and analyzing univariate time series forecasts including exponential smoothing via state-space

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

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 modeling. Explore the gas (Australian monthly gas production)  dataset in the Forecast package to do the following:

[Hint code]

install.packages("forecast")

library(forecast)

data<- forecast::gas

• 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)

o HINT: Please use the dataset from January 1970 for your analysis.

o Please partition your dataset in such a way that you have the data 1994 onwards in the test data.

• 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 initial forecast for next 20 periods. Check the same using the various metrics, after finalising the model, develop a final forecast for the 12 time periods. Use both manual and auto.arima (Show & explain all the steps) (20 marks)

o HINT: You can apply auto.arima(Train_data (refers to the train data set), seasonal=TRUE if seasonality is present in the data, FALSE is seasonality is not present.)

• Report the accuracy of the model (5 marks) 

Please note the following:

• You have to submit 2 files : 

1. Business Report: In this, you need to submit all the answers to all the questions in a sequential manner. It should include the detailed explanation of approach used, insights, inferences, all outputs of codes like graphs, tables etc. Your report should not be filled with codes. You will be evaluated based on the business report.

2. R code file : This is a must and will be used for reference while evaluating

• 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.

Scoring guide (Rubric) - Project - TSF (1)

Criteria Points

1. Read the data as a time series object in R. Plot the data 5

2. What do you observe? Which components of the time series are present in this dataset? 5

3. What is the periodicity of dataset? 5

4. 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

5. Develop an ARIMA Model to forecast for next 12 periods. Use both manual and auto.arima (Show & explain all the steps) 20

6. Report the accuracy of the model 5

Points 60

Attachments:

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