ARIMA (Autoregressive Integrated Moving Average):
In an autoregressive model, the forecast of a variable is based on its linear combination of past values of the same variable.
- Autoregressive (AR): ARIMA looks at the relationship between the current value of a time series and its past value. It considers how the past values of a variable contribute to predicting its future values.
- If yesterday’s stock prices influenced today’s, ARIMA captures this influence.
- Integrated (I): It aims to make the time series data stationary, meaning its statistical properties like mean and variance remain constant over time. It checks if the data has a trend or changing statistical properties.
- If the data is not stationary, ARIMA applies differencing to make it more predictable.
- Moving Average (MA): It involves considering the past forecast errors to predict future values. Instead of looking at past values of the variable, ARIMA looks at past errors in predicting the variable.
- It considers how the errors from previous predictions influence the current prediction.
Understanding ARIMA in Simple Steps:
- Start with the Data:
- Data should be more like having a series of numbers, like daily stock prices or monthly website visits—something that changes over time.
- Understand Trends:
- ARIMA looks at the overall trend in the data. It analysis if the trend is generally going up or down. This helps to understand the baseline behavior.
- Deal with Trends:
- If there’s a trend, ARIMA helps to remove it by differencing.
- Autoregressive Part:
- It checks how today’s data relates to the data from the past. If there’s a clear connection, it uses this to make predictions.
- Moving Average Part:
- ARIMA considers how accurate past predictions were and adjusts for any mistakes made. It’s a way of learning from past experiences.
- Combine Everything for Predictions:
- The algorithm combines the insights from trends, historical data, and past predictions to make an informed guess about what might happen next