https://github.com/marta-barea/time-series-analysis-r

https://github.com/marta-barea/time-series-analysis-r

r rprogramming time-series time-series-analysis

Last synced: 3 months ago
JSON representation

• Host: GitHub
• URL: https://github.com/marta-barea/time-series-analysis-r
• Owner: Marta-Barea
• License: gpl-3.0
• Created: 2023-01-16T08:37:48.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-11-20T17:44:03.000Z (11 months ago)
• Last Synced: 2024-11-20T18:34:11.046Z (11 months ago)
• Topics: r, rprogramming, time-series, time-series-analysis
• Language: R
• Homepage:
• Size: 231 KB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# **time-series-r**

This repository contains examples, analysis, and tools for working with **time series** using the R programming language. It is designed for both beginners who want to learn about time series analysis and advanced users looking for practical resources and models.

---

## **Contents**

The repository includes:

1. **Introduction to Time Series:**

   - Basic concepts: seasonality, trends, noise.

   - Data exploration and visualization.

2. **Data Preprocessing:**

   - Cleaning and transformation methods.

   - Handling missing data.

   - Time series decomposition.

3. **Time Series Models:**

   - ARIMA (AutoRegressive Integrated Moving Average).

   - SARIMA (Seasonal ARIMA).

   - Exponential Smoothing models.

   - Machine Learning-based methods (optional).

4. **Model Evaluation:**

   - Performance metrics such as MAE, RMSE, and MAPE.

   - Cross-validation for time series.

5. **Practical Examples:**

   - Financial data forecasting.

   - Sales time series analysis.

   - Real-world case studies.

  

---

## **Requirements**

To use the scripts in this repository, you will need:

- **R** (version 4.0 or higher).

- **RStudio** (optional, but recommended).

- The following R packages:

  - `forecast`

  - `ggplot2`

  - `tseries`

  - `dplyr`

  - `tidyr`

Install the required packages by running:

```R

 install.packages(c("forecast", "ggplot2", "tseries", "dplyr", "tidyr"))

```

---

## **Usage**

1. Clone the repository:

```bash

   git clone https://github.com/Marta-Barea/time-series-analysis-r.git

   cd time-series-analysis-r

```

3. Open the scripts in RStudio to explore and execute the examples.

4. Follow the inline comments in the scripts to understand the analysis flow.

---

### 📜 **License**

This project is licensed under the GNU GENERAL PUBLIC License. See `LICENSE` for details.