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https://github.com/loccx78vn/forecasting_time_series
You will learn about building ARIMA and Ensemble Machine Learning to forecast time series data in R
https://github.com/loccx78vn/forecasting_time_series
demand-forecasting machine-learning supply-chain-management
Last synced: 21 days ago
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You will learn about building ARIMA and Ensemble Machine Learning to forecast time series data in R
- Host: GitHub
- URL: https://github.com/loccx78vn/forecasting_time_series
- Owner: Loccx78vn
- Created: 2024-08-20T15:40:44.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-09-16T17:02:57.000Z (4 months ago)
- Last Synced: 2024-09-16T20:58:35.900Z (4 months ago)
- Topics: demand-forecasting, machine-learning, supply-chain-management
- Language: TeX
- Homepage: https://loccx78vn.github.io/Forecasting_time_series/
- Size: 13 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Time series in R:
Time series analysis in RStudio can be quite powerful, allowing you to uncover trends, seasonal patterns, and other insights from your data. Below is a basic tutorial to get you started with time series analysis in RStudio. We'll cover:
- Getting Started with Time Series Data
- Visualizing Time Series Data
- Decomposing Time Series
- Building Time Series Models: ARIMA (AutoRegressive Integrated Moving Average) and ETS (Exponential Smoothing State Space Models) are commonly used for time series forecasting.
- Forecasting: Use models to predict future values. R provides functions for generating forecasts and visualizing the forecasted data.
Evaluation.
- Evaluate model performance using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and others.
# Introduction to Time Series Analysis in RStudio:
## Getting Started in RStudio:
RStudio provides an integrated environment for data analysis in R, including time series analysis. Here’s a high-level overview of how to approach time series analysis using RStudio:
## What is Time Series Analysis?
Time series analysis involves techniques for analyzing time-ordered data points to extract meaningful statistics and identify patterns such as trends, seasonal effects, and cyclical behaviors. This analysis helps in understanding past behavior and making forecasts about future values.
## Key Concepts in Time Series Analysis
- Trend: The long-term movement in the data. It could be upward, downward, or constant over time.
- Seasonality: Regular patterns that repeat over a fixed period, such as daily, monthly, or yearly.
- Cyclical Patterns: Long-term fluctuations not fixed in a regular period, often influenced by economic or business cycles.
- Residuals: The noise or random variation left after removing the trend and seasonality.
# Conclusion:
I hope you can enjoy the pratice and hope you have a great day!!! See you soon in another future topic.
