Abstract: ATA method is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing methods. ATA method’s forecasting performance is superior to existing methods both in terms of easy implementation and accurate forecasting. In this project, ATA method has been proposed for all types of trend and it has been made suitable for seasonal or non-seasonal time series, where the deseasonalization can be performed via classical multiplicative decomposition method. At the same time, two different software have been developed in R and PHP programming languages and have been made available to all users. Also, the R package “ATAforecasting” has been developed as a comprehensive toolkit for automatic time series forecasting. Finally, by performing a model selection amongst these proposed models the performance of ATA method’s performance on M3-competition (Makridakis & Hidon (2000)) has been compared to other existing methods. In addition, ATA competed in M4-competiiton with five different models. According to various error metrics, the models performed better than their exponential smoothing based counters. Despite their simplicity, they have been ranked satisfactorily high compared to the other methods.