ARIMA
which stands for Auto Regressive Integrated Moving Average, is like a smart tool for predicting future values in a timeline, such as stock prices or weather patterns.
Auto Regressive (AR) Component: Think of this as looking at how today’s situation is connected to what happened in the past. If we say it’s AR(3), it means we’re looking at the connection between today and the past three days.
Integrated (I) Component: This part is about making sure our data is easy to work with. We do this by checking the difference between consecutive days. If we do it twice, it’s like looking at the change in change.
Moving Average (MA) Component: Here, we consider how today’s situation relates to any mistakes we made in predicting the past. If it’s MA(2), it means we’re looking at the connection between today and the errors we made in predicting the two previous days. The ARIMA model is just a combination of these three, written as ARIMA(p, d, q), where: p is about looking back in time. d is about making the data easy to work with. q is about learning from our past mistakes.
Steps in Building an ARIMA Model:
Inspecting Data: Look at how things have been changing over time. Making Things Simple: If things are too complicated, we simplify them by looking at differences between days.
Choosing Settings: Figure out how much we need to look back ( p), how many times to simplify ( d), and how much to learn from past mistakes ( q). Putting it All Together: Use these settings to build a smart model that learns from the past. Checking How Well It Works: See how well our model predicts the future by comparing it to new data.
Making Predictions: Once it’s working well, we can use our smart model to make predictions about what might happen next. ARIMA models are great for making predictions when things follow a clear pattern. However, for more complicated situations, there are other tools that might work even better.