STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM

Abstract

It is difficult to forecast vegetable prices because they are affected by numerous factors, such as weather and crop production, and the time-series data have strong non-linear and non-stationary characteristics. To address these issues, we propose the STL-ATTLSTM (STL-Attention-based LSTM) model, which integrates the seasonal trend decomposition using the Loess (STL) preprocessing method and attention mechanism based on long short-term memory (LSTM). The proposed STL-ATTLSTM forecasts monthly vegetable prices using various types of information, such as vegetable prices, weather information of the main production areas, and market trading volumes. The STL method decomposes time-series vegetable price data into trend, seasonality, and remainder components. It uses the remainder component by removing the trend and seasonality components. In the model training process, attention weights are assigned to all input variables; thus, the model’s prediction performance is improved by focusing on the variables that affect the prediction results. The proposed STL-ATTLSTM was applied to five crops, namely cabbage, radish, onion, hot pepper, and garlic, and its performance was compared to three benchmark models (i.e., LSTM, attention LSTM, and STL-LSTM). The performance results show that the LSTM model combined with the STL method (STL-LSTM) achieved a 12% higher prediction accuracy than the attention LSTM model that did not use the STL method and solved the prediction lag arising from high seasonality. The attention LSTM model improved the prediction accuracy by approximately 4% to 5% compared to the LSTM model. The STL-ATTLSTM model achieved the best performance, with an average root mean square error (RMSE) of 380, and an average mean absolute percentage error (MAPE) of 7%.

A Heterogeneous Graph Enhanced LSTM Network for Hog Price Prediction Using Online Discussion

“…We briefly discuss these competing methods here. [37], STL-Attention-based LSTM is a stateof-the-art method to forecast the price of agricultural products. In their original paper, STL-ATTLSTM makes use of several types of information to forecast monthly vegetable prices, such as vegetable prices, weather information, and market trading volumes [37].…”

“…[37], STL-Attention-based LSTM is a stateof-the-art method to forecast the price of agricultural products. In their original paper, STL-ATTLSTM makes use of several types of information to forecast monthly vegetable prices, such as vegetable prices, weather information, and market trading volumes [37]. According to their paper, the STL algorithm decomposes the price series into three parts: trend, seasonality, and remainder components.…”

“…As in Table 2, Single LSTM, STL-ATTLSTM, and our HGLSTM all contain LSTM networks, accounting for their better performance than MLP. Yin et al [37] integrates the STL method and attention mechanism into LSTM. Therefore, their STL-ATTLSTM outperforms Single LSTM.…”

A Bibliometric Analysis of Literature on Vegetable Prices at Domestic and International Markets—A Knowledge Graph Approach

“…Paredes-Garcia et al (2019) implement a vegetable price prediction system to provide information for Mexican farmers to make better decisions [37]. Similarly, Yin et al (2020) use STL and attention mechanism-based LSTM to predict vegetable prices with small errors [38]. Scholars are based on HP filtering the hybrid neural network structure of the processor [39] predicts the price of vegetables in linear and nonlinear modes, effectively reducing the risk of vegetable farmers; scholars also combine the fruit fly algorithm (FOA) with the induced ordered weighted average (IWOA), improve the prediction accuracy [40], by establishing a seasonal SARIMA model to predict the monthly price of tomato wholesale prices [32].…”

An AI Based Web Portal for Cotton Price Analysis and Prediction

“…In addition, a study of Cho's Expands the field of analysis and adds to the model weather variables, enhancing the cotton Prediction of spot prices, based on some supply factors such as (quality and weather) [32][33][34][35]. The tworesearch hedonic regression model applied by Cho and Ethridge and Davis [36,37], an Alternate type of multiple regression model of ordinary least squares (OLS) to estimate Based on categorical variables, the price of cotton While both of the two tests are related to attributes of cotton quality [38][39][40][41][42].…”