An Overview on Evaluation Methods of Sequence Prediction Problems

Sequence prediction problems are prevalent in various domains, including natural language processing, time series analysis, bioinformatics, and condition-based maintenance. Evaluating the performance of sequence prediction models is crucial to assess their accuracy, robustness, and generalization capabilities. This paper presents an overview of evaluation methods used for sequence prediction problems. Throughout the paper, we emphasize the importance of selecting suitable evaluation methods that align with the specific characteristics and goals of typical sequence prediction problems. We also provide insights into the considerations associated with each evaluation method. The paper discusses the fundamental metrics commonly employed, such as accuracy, precision, recall, and F1-score, which provide insights into the overall performance of sequence prediction models. Additionally, some more specialized metrics tailored to sequence prediction, are presented. These metrics account for the unique characteristics and challenges of sequence data. In the paper evaluation techniques specific to distinct types of sequence prediction problems are evaluate such as, perplexity, BLEU score, and ROUGE score which are widely used to evaluate language models and machine translation systems. In time series analysis, metrics such as mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) are commonly employed.