Since the size of the error is expressed as a percentage value, it can be used to understand the performance of the model. Then, take the absolute value of the relative size of the error for each actual value and calculate the mean. In order to calculate MAPE, first calculate the relative size of the error compared to the actual values by dividing the difference between each of the actual values and the predicted value by each actual value. In chapters 4 and 5, we will use deep learning models to predict future confirmed cases. In chapter 2, we will deepen our understanding of COVID-19 confirmed case data through exploratory data analysis, and in chapter 3 we will learn how to restructure time series data so it can be used for supervised learning. In chapter 1, we will look at the neural network structures that can be used when building a time series prediction model, and check the metrics that can be used when evaluating model performance. In this tutorial, we will build a model that predicts future confirmed cases of COVID-19 based on data from the past confirmed cases using COVID-19 confirmed case data provided by Johns Hopkins University’s Center for Systems Science and Engineering. In addition, stakeholders in the retail domain are interested in predicting the sales volumes of items for efficient product management, which was also the topic of a data science competition.( distribution) In fact, these issues were presented for data science competitions ( electric power, city gas) to facilitate the discovery of new models. Electric power plants need to predict future power demand to secure sufficient amounts of reserved power, and city gas companies need a future usage prediction model to take preemptive measures against meter reader failure and meter reader cheating. One of the areas is in the energy domain. Time series prediction is a skill that is required in many areas. Therefore, in this chapter, we will build a model that predicts future values through supervised learning based on a neural network structure. It can be defined as a supervised learning problem in that it requires finding patterns between observed data in the past and future values. Time series prediction is the prediction of future values based on observed values in the past.
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