Time Series Analysis Projects in R

Project Structure

.
├── AS1
│   ├── MATH1318 Time Series Analysis - Assignment - 1.pdf
│   ├── assignment1Data2023.csv
│   └── assignment1Solution.Rmd
├── AS2
│   ├── BC_arctic_sea_ice_ts.png
│   ├── assignment2Data2023.csv
│   ├── assignment2Solution.Rmd
│   ├── first_differenced_arctic_sea_ice_plot.png
│   └── s3879312 - AS2 - MATH1318 Time Series Analysis.pdf
├── AS3
│   ├── Final Project - MATH1318 Time Series Analysis.pdf
│   ├── LICENSE
│   ├── README.md
│   ├── R_code
│   │   ├── BC_weatherAUS_ts.png
│   │   └── Time Series Models of Heteroscedasticity.Rmd
│   ├── data
│   │   ├── cleaned
│   │   │   └── cleaned_weatherAUS.csv
│   │   └── raw
│   │       └── weatherAUS.csv
├── LICENSE
└── README.md

Projects

• AS1 This project involves analysis of the return on a share market trader's investment portfolio. The dataset represents a trader's portfolio's return (in AUD100,000), comprising 127 observations from the same year and on consecutive trading days. The objective is to find the best fitting model among the linear, quadratic, cosine, cyclical, or seasonal trend models and make predictions for the next 15 trading days.

• AS2 This project analyzes the yearly Arctic sea ice minimum extent (million square km) from 1979 to 2022. This data is obtained from NASA. The task involves proposing a set of possible ARIMA(p, d, q) models using model specification tools and fitting the models to find parameter estimates.

• AS3 - Final Project For the final project, a time series dataset is selected, a research question is set, the data is analyzed and predictions for the next 10 units of time are made. This involves descriptive analysis, visualization, model specification, model fitting and selection, diagnostic checking and interpretation of results.

How to Use

• Clone the repository or download the projects.
• Open the R markdown files in your R environment.
• Run the R scripts or notebooks to replicate the analysis.
• You can modify the code as needed.