相关实验视频
Updated: Sep 17, 2025
13:19
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
9.4K
通过深度学习改进农业商品价格预测.
R L Manogna1, Vijay Dharmaji2, S Sarang2
1Department of Economics and Finance, Birla Institute of Technology and Science, Pilani, K K Birla Goa Campus, Zuari nagar, Sancoale, 403726, Goa, India. manognar@goa.bits-pilani.ac.in.
Scientific reports
|July 2, 2025
概括
深度学习模型,如长期短期记忆 (LSTM) 和门式循环单位 (GRU),显著提高农业大宗商品价格预测的准确性. 这些先进的方法在预测价格波动方面优于传统模型,有助于市场规划.
科学领域:
- 农业经济学 农业经济学
- 数据科学数据科学数据科学
- 时间序列预测时间序列预测
背景情况:
- 准确的农业商品价格预测对于农业依赖经济体的市场规划和政策至关重要.
- 受天气和市场需求影响的价格波动给传统预测方法带来了重大挑战.
- 为了应对这些挑战,需要对各种预测模型进行全面评估.
研究的目的:
- 评估和比较传统的随机,机器学习和深度学习模型的性能,用于农业商品价格预测.
- 确定最有效的模型来捕捉复杂的时间模式和大宗商品价格波动.
- 为改善市场干预,作物规划和风险管理策略提供见解.
主要方法:
- 从2010年1月到2024年6月,利用了23种商品的每日批发价格数据.
- 评估了传统模型 (ARIMA),机器学习 (SVR,XGBoost) 和深度学习方法 (MLP,RNN,LSTM,GRU,ESN).
- 使用错误指标比较模型性能,例如根平均平方错误 (RMSE) 和平均绝对百分比错误 (MAPE).
主要成果:
- 深度学习模型,特别是长期短期记忆 (LSTM) 和门式循环单元 (GRU),表现出卓越的预测准确性.
- 与ARIMA模型相比,GRU在洋和西红等商品中实现了显著较低的RMSE和MAPE.
- 深度学习模型有效地捕获了农业商品价格固有的复杂时间模式和非线性动态.
结论:
- 深度学习技术,特别是LSTM和GRU,为预测农业大宗商品价格波动提供了更可靠的方法.
- 这些发现支持加强市场干预,更好地规划作物,并为利益相关者提供更有效的风险管理.
- 未来的研究应该探索混合模型,并纳入外部数据,如天气信息,以进一步提高预测的准确性.
相关概念视频
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
02:16Light Acquisition
8.6K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.6K
02:52Predicting Products: Substitution vs. Elimination
12.3K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
The following factors can influence the mechanisms competing against each other:
12.3K
01:25Multiple Regression
3.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.2K
01:22Upsampling
322
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
322
01:29Aggregates Classification
389
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
389