Time-Series-Analysis

Perform necessary data preprocessing on the daily closing prices data of various stock exchanges. Learn about various types of time sereis like Random Walk, White Noise, Stationary, Non-stationary, etc. Check for stationarity using Augmented Dickey Fuller test. Determine the optimal number of lags required in a model using AutoCorrelation Function (ACF) and Partial AutoCorrelation Function (PACF) and further optimise it by analysing the residuals from a model. Fit various time series models like AR, MA, ARMA, ARIMA, ARIMAX, SARIMAX to determine the one that best explains the given data and also see how AUTO ARIMA can simplify the task of finding the best model. Use ARCH and GARCH models to predict volatility. Forecast for future values based on past data.