Have you ever wondered how businesses and researchers can accurately predict future trends and behaviors? One way is through time series forecasting methods such as ARIMA and KNN. Both methods have their strengths and weaknesses, and in this blog post, we compare these two popular techniques.

ARIMA, or Autoregressive Integrated Moving Average, is a statistical model used to analyze and forecast time series data. It considers three factors: autoregression, or the relationship between a variable and its past values; integration, or the differencing of the data to make it stationary; and moving average, or the relationship between the error terms in a time series. ARIMA is widely used in fields such as finance, economics, and engineering due to its ability to capture long-term trends and seasonal patterns.

On the other hand, KNN, or K-Nearest Neighbors, is a machine-learning algorithm used for classification and regression. It works by finding the K closest points in the training data to a given input and using their values to predict the output. In time series forecasting, KNN is used to find the K closest historical data points to a current point and use their values to predict the future. KNN is known for its simplicity and flexibility, making it popular in fields such as healthcare and marketing.

So, what are the pros and cons of each method? ARIMA has the advantage of capturing complex time series patterns and trends, making it useful for long-term forecasting. However, ARIMA requires a stationary time series, which is not feasible in all cases. Additionally, ARIMA can be difficult to interpret and requires expertise in statistical modeling.

KNN, on the other hand, is easy to understand and implement, making it accessible to a wider range of users. It also does not require the data to be stationary, making it more versatile in some cases. However, KNN is sensitive to noise and outliers in the data, which can result in inaccurate predictions. It is also not as effective at capturing long-term trends as ARIMA.

You can see how the two algorithms worked in an anonymized representation of one of our projects.

Despite the fact, ARIMA is a state-of-the-art technique used for forecasting, it did not work very well for our use case, however, KNN could provide good results out of the box. We could've probably gotten ARIMA to perform better if we invested more effort and time.

In conclusion, there is a tradeoff between sophisticated and complex versus pragmatic and simple algorithms. KNN is definitely an effective first step.