In this blog we are going to recall the Random Walk and then we are going to apply it on the Economic Indicators to see how well it will predict the incoming intl flights.

The fundamental idea behind the concept is that future values in a time series depend on the value that was most recently observed, and that any deviation from this value is essentially random.  Despite its simplicity, forecasting models are evaluated using the random walk, especially when it is challenging to predict complex patterns.

Initialization: To start the forecasting process, it is often necessary to use the most recent observed value from the historical data.Iterative Prediction: For every subsequent time interval, the forecast for the subsequent observation is simply the most recent observed value. The model assumes that any changes or deviations are random and unpredictable.Evaluation: Metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE) are frequently used to assess the performance of the random walk model by comparing the predicted values to the actual observations.

Using Mean Absolute Error (MAE), the evaluation reveals an average prediction deviation from actual international flight counts of about 280.16 units. When more complex models are compared to this MAE, their precision improves over the simplistic random walk.

The random walk’s average prediction error is approximately 5.33% of the maximum flight count when scaled. With an MAE of approximately 578.06, the random walk outperforms a basic mean benchmark model with an average of around 3940.51 international flights at Logan Airport. This demonstrates how the random walk can capture more information than a mean-predicting model.

A relative measure is given by the scale-adjusted MAE of 5.33%, which shows that, on average, the random walk’s prediction errors are low in relation to the maximum flight count. The way you interpret MAE should be in line with the particular context of your data and the needs you have for forecasting.