One of the most important concept of Data Science is to be able to analyze and forecast time series data.
The main file called arima_forecasting.R is from ('https://www.datascience.com/blog/introduction-to-forecasting-with-arima-in-r-learn-data-science-tutorials').
Eventually, I will be creating my own stock price analysis here since I am very interested in the performance of these models.
- Seasonality - Fluctuations in the data related to calendar cycles
- Trend - Overall pattern of the series
- Cycle - Decreasing or Increasing patterns that are not seasonal
- ADF - Augmented Dickey-Fuller Test is a formal statistical test for stationarity.
- AcF - Autocorrelation Plots determines whether a series is stationary
- Pacf - Partial Autocorrelation Plots
Basic cloning of repository,
git clone https://github.com/jjneojiajun/time_series_data.git
This is a R-Script, this meant that you can either Ctrl-A (Windows) or Command-A (Mac) and run the script or do it step by step.
You need R-Studio and R to be able to run the R File.