Nifty 50 Time Series Forecasting using AUTO ARIMA + FACEBOOKS PROPHET + LightGBM
As the name suggests, an ordered set of observations made over a period of time is time series. Since time-series contain sequential data points mapped at successive time duration, it can be a very important tool for making predictions. Some of its major application areas include — stocks and financial trading, analysing online and offline retail sales, and medical records such as heart rate, EKG, MRI, and ECG.
Time series dataset evolves lots of enthusiam between data scientist. They are many different ways to approach a Time series problem. Following models are explored in the below notebook
- AUTO ARIMA
- Proprhet
- LightGBM
The data is the price history and trading volumes of the fifty stocks in the index NIFTY 50 from NSE (National Stock Exchange) India. All datasets are at a day-level with pricing and trading values split across .cvs files for each stock along with a metadata file with some macro-information about the stocks itself. The data spans from 1st January, 2000 to 30th April, 2021.
The dataset contains the following features
- Date - Trading day
- Symbol - Stock Name
- Prev Close - Closing price of the stock on previous day
- Open - Openning Price on the given day
- High - Highest Price on the given day
- Low - Lowest Price on the given day
- Last - Last Price on the given day
- Close - Closing Price on the given day
- VWAP - Average price on the stock traded througout the day
Since we have data of 50 stocks avaliable, It would make sense to pick one stock at a time and perform the analysis, Hence I am picking Titan for my analysis