## 1 Introduction  
Agricultural commodity prices are volatile, and they are affected by various factors, including weather conditions, the planting area, consumption trends, and policy factors [1]. Price volatility makes it difficult for producers to make sales decisions, which leads to instability in producer incomes and consumer burdens on purchasing behavior. Therefore, accurate forecasting of agricultural supply and demand is essential for both agricultural producers and consumers. To reduce volatility, it is crucial to establish an agricultural production plan at the beginning of the production process by forecasting supply and demand [2; 3; 4]. Forecasts of the sales volumes of agricultural products can use historical time-series data to reduce risk at the production stages and reduce price fluctuations after production [5]. However, the trends of the agricultural market are complicated and nonlinear as a result of numerous factors, including economic globalization, financial speculation, climate change, and oil price fluctuations. This complexity makes it increasingly difficult to accurately predict agricultural supply and demand [1; 5]. Since it is very difficult to predict agricultural supply and demand with different variability factors, more research needs to be conducted on such predictions. By contrast, studies on energy and electrical load predictions, which have less volatile factors than agricultural predictions, are actively conducted. Specifically, various studies have examined the prediction of energy and electric loads, which play an important role in energy management systems [6; 7; 8; 9; 10; 11]. The studies on other applications include rainfall forecasting [12], bitcoin price forecasting [13], bankruptcy prediction [14], and stock price prediction [15; 16]. Agricultural product sales are mainly time series, and many general time series forecasting methods can be applied to forecast them. The problem of predicting the supply and demand of agricultural products is mostly solved using classical statistical models or machine learning models. The machine learning models are typically implemented with recently emerging deep learning models, such as the recurrent neural network. In this paper, we propose an agricultural sales forecasting system based on deep learning to stabilize agricultural supply and demand. The time-series data for the sales volume of agriculture products contain a high degree of weekly and monthly seasonality. The conventional models, such as seasonal autoregressive integrated moving average (SARIMA) and LSTM (long short-term memory), have the ability to model such seasonality. The SARIMA model represents the seasonal information statistically with its formulation. However, the SARIMA model has no ability to accept \"explicit\" seasonal information. An example of explicit seasonal information is that the current time step is May and Friday. The LSTM model is currently very popular as a deep learning model for processing various kinds of time-series data and natural languages [17]. The state transition and gate mechanisms of LSTM can implicitly capture the seasonal information in the data. We observe that the explicit seasonal information can be additionally input to LSTM through the input layer and that this idea will improve the accuracy. This observation motivates our research. The proposed LSTM model is a prediction method that adds weekly, monthly, and quarterly features to the existing LSTM method. We call the proposed LSTM model seasonal LSTM (SLSTM). The seasonality information, namely, weekly, monthly, and quarterly features, are input into the SLSTM either individually or simultaneously. In our experiment, the forecasting performance is evaluated in terms of three error metrics: mean absolute error (MAE), root mean squared error (RMSE), and normalization mean absolute error (NMAE). The proposed SLSTM model shows a lower error rate than the conventional methods. Section 2 presents a literature review. Section 3 describes the dataset used in our experiments.