Time Series & Forecasting Note/ ARIMA, ACG, PACF, EXPONENTIAL SMOOTHING, REGRESSION, WHITE NOISE

These handwritten notes cover all important concepts of Time Series and Forecasting in a clear and exam-oriented manner. The document includes detailed explanations, formulas, and solved examples for key topics such as exponential smoothing, moving averages, ARIMA/ARMA models, ACF and PACF analysis, stationarity, and white noise. It also covers regression-related concepts including multicollinearity, heteroscedasticity, VIF, and OLS assumptions, along with statistical measures like MAE, MSE, MAPE, and Theil’s U statistic for evaluating forecasting accuracy. The notes are structured for quick revision and are especially useful for MSc students, data science learners, and anyone preparing for exams in time series analysis. Key features: * Easy-to-understand handwritten format * Step-by-step problem solving * Covers both theory and numerical examples * Includes important exam topics and formulas Perfect for last-minute revision and concept clarity.