Title: A Decomposition Based Dual Projection Model for Multivariate Time Series Forecasting and Anomaly Detection. Abstract: Efficient anomaly detection and diagnosis in multivariate time series data is of great importance for various application areas. Forecasting of long-sequence time series is an important problem to prepare for future changes. An accurate prediction can help to detect anomaly events beforehand and make better decisions. It seems that one has to use more complex structures for deep learning models to get better performance, e.g., the recent surge of Transformer variants for time series modeling. However, such complex architectures require a large amount of training data and extensive computing resources. In addition, many of the considerations behind such architectures do not hold for time series applications. The objective of this study is to re-consider the effectiveness of deep learning architectures for efficient and accurate time series forecasting and anomaly detection. A model with direct projections is proposed, and it outperforms existing Transformer based models in most cases by a significant margin. The new decomposition based dual projection (DBDP) model consists of an anchored global profile and a varied number of decomposed seasonal local profiles of the time series for better forecasting performance. In addition to forecasting, a non-contrastive self-supervised learning approach, we propose to include a contrastive learning module in the DBDPC model for better forecasting performance and robustness. Finally, we apply the DBDP and DBDPC models to forecasting based time series anomaly detection and achieve superior performance over the latest SoTA models. These results demonstrate the effectiveness of the several key considerations behind the DBDP and DBDPC models, which also encourages the development of new architectures for time series applications.