Modelling and forecasting time series data using ATA method

It is difficult to make predictions especially about the future and making good predictions is not always easy. However, better predictions remain the foundation of all science therefore the development of accurate, robust and reliable forecasting methods is very important. Numerous number of forecasting methods have been proposed and studied in the literature. There are still two dominant major forecasting methods: Box-Jenkins ARIMA and Exponential Smoothing (ES), methods are derived or inspired from them. After more than 50 years of widespread use, exponential smoothing is still one of the most practically relevant forecasting methods available due to their simplicity, robustness and accuracy as automatic forecasting procedures especially in the famous M-Competitions. The well-known fact in these competitions is ES has a proven success against more complex ARIMA models. Despite its success and widespread use in many areas, ES models have some shortcomings that negatively affect the accuracy of forecasts. Therefore, a new forecasting method in this thesis will be proposed to cope with these shortcomings and it will be called ATA method. This new method is obtained from traditional ES models by modifying the smoothing parameters therefore both methods have same structural forms and ATA can be easily adapted to all of the individual ES models. The two methods will be compared on popular metrics that are commonly used for evaluating performance of forecasting techniques. It will be shown that ATA models have better performance in terms of accuracy, simplicity, speed and interpretability. The performance of ATA method will be compared not only to ES but also to other most successful competitors according to different performance criterions on the famous M3-Competition data set since it is still the most recent and comprehensive time-series data collection available.