ARIMA Models:
As part of our Project-3, we are planning to focus on Time Series Forecasting and use models such as ARIMA. Autoregressive Integrated Moving Average (ARIMA) models are a class of statistical models widely used in time series analysis and forecasting. Developed to capture and describe the temporal dependencies present in a time series dataset, ARIMA models are a combination of autoregressive (AR) and moving average (MA) components, with an added differencing step for stationarity.
The “AR” in ARIMA refers to the autoregressive component, which implies that the current value of the time series is dependent on its previous values. The “MA” stands for the moving average component, indicating that the current value is also influenced by a stochastic term representing the past forecast errors. The “I” in ARIMA represents differencing, a crucial step to transform a non-stationary time series into a stationary one. This differencing helps stabilize the mean and variance of the time series, making it amenable to modeling.
ARIMA models are denoted as ARIMA(p, d, q), where “p” is the order of the autoregressive component, “d” is the order of differencing, and “q” is the order of the moving average component. The appropriate choice of these parameters depends on the characteristics of the specific time series being analyzed. ARIMA models have proven effective in various fields, including economics, finance, and environmental science, making them a valuable tool for researchers and analysts seeking to make accurate predictions based on historical time series data.