In delving deeper into time series analysis, I’ve explored topics such as autocorrelation, forecasting, and cyclical analysis. In considering regression analysis, I’ve concluded that conventional linear regression may not be ideal for time series research, especially when dealing with data exhibiting seasonality, trends, or temporal dependencies. Time series regression is better approached with techniques like autoregressive integrated moving average (ARIMA) models, which account for the inherent characteristics of time series data.
Autocorrelation Function (ACF): A statistical tool, ACF assesses the correlation between a time series and its own lagged values. It aids in identifying patterns, trends, and seasonality by displaying correlation coefficients for different lags, highlighting significant delays and autocorrelation patterns.
Forecasting: The forecasting process often begins with using the most recent observed value from historical data. Iterative prediction involves projecting subsequent observations based on the assumption that changes or deviations are random and unpredictable.
Evaluation: Performance assessment of the forecasting model is done using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE). These metrics compare projected values to actual observations, providing insights into the model’s accuracy.