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01:03Prediction Intervals
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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.
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02:16Light Acquisition
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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.
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02:52Predicting Products: Substitution vs. Elimination
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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:
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01:25Multiple Regression
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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...
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01:22Upsampling
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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...
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01:29Aggregates Classification
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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...
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Deep Neural Networks for Image-Based Dietary Assessment
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Enhancing agricultural commodity price forecasting with deep learning.
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
Summary
Deep learning models, like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), significantly improve agricultural commodity price forecasting accuracy. These advanced methods outperform traditional models in predicting volatile prices, aiding market planning.
Area of Science:
- Agricultural Economics
- Data Science
- Time Series Forecasting
Background:
- Accurate agricultural commodity price forecasting is crucial for market planning and policy in agriculture-dependent economies.
- Price volatility, influenced by weather and market demand, presents significant challenges for traditional forecasting methods.
- A comprehensive evaluation of various forecasting models is needed to address these challenges.
Purpose of the Study:
- To evaluate and compare the performance of traditional stochastic, machine learning, and deep learning models for agricultural commodity price forecasting.
- To identify the most effective models for capturing complex temporal patterns and volatility in commodity prices.
- To provide insights for improving market interventions, crop planning, and risk management strategies.
Main Methods:
- Utilized daily wholesale price data for 23 commodities from January 2010 to June 2024.
- Assessed traditional models (ARIMA), machine learning (SVR, XGBoost), and deep learning approaches (MLP, RNN, LSTM, GRU, ESN).
- Compared model performance using error metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE).
Main Results:
- Deep learning models, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), demonstrated superior forecasting accuracy.
- GRU achieved significantly lower RMSE and MAPE for commodities like onions and tomatoes compared to the ARIMA model.
- Deep learning models effectively captured complex temporal patterns and nonlinear dynamics inherent in agricultural commodity prices.
Conclusions:
- Deep learning techniques, particularly LSTM and GRU, offer a more reliable approach to forecasting volatile agricultural commodity prices.
- These findings support enhanced market interventions, better crop planning, and more effective risk management for stakeholders.
- Future research should explore hybrid models and incorporate external data, such as weather information, to further boost forecasting accuracy.