Forecasting the Price of Rice in Banda Aceh after Covid-19

Paper: arXiv 2411.15228

Abstract

This research aims to predict the price of rice in Banda Aceh after the occurrence of Covid-19. The last observation carried forward (LOCF) imputation technique has been used to solve the problem of missing values from this research data. Furthermore, the technique used to forecast rice prices in Banda Aceh is auto-ARIMA which is the best ARIMA model based on AIC, AICC, or BIC values. The results of this research show that the ARIMA model (0,0,5) is the best model to predict the prices of lower quality rice I (BKB1), lower quality rice II (BKB2), medium quality rice I (BKM1), medium quality rice II (BKM2), super quality rice I (BKS1), and super quality rice II (BKS2). Based on this model, the results of forecasting rice prices for all qualities show that there was a decline for some time (between September 1, 2023 and September 6, 2023) and then remained constant (between September 6, 2023 and December 31, 2023).

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 6.0/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper uses relatively simple ARIMA models with basic formulae and no advanced mathematical derivations, but it applies standard backtesting practices with a real dataset, imputation techniques, and explicit forecasting results, making it highly practical for implementation.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Predict Rice Prices in Banda Aceh<br>Post-COVID-19"] --> B["Data Preprocessing:<br>LOCF Imputation for Missing Values"]
  B --> C["Model Selection:<br>Auto-ARIMA Optimization<br>(Minimizing AIC/BIC)"]
  C --> D["Model Identified:<br>ARIMA(0,0,5)"]
  D --> E["Forecasting Results"]
  
  subgraph E ["Forecasting Results"]
      F["Price Decline:<br>Sep 1 - Sep 6, 2023"]
      G["Price Stabilization:<br>Sep 6 - Dec 31, 2023"]
      H["Applies to all rice qualities<br>(BKB1, BKB2, BKM1, BKM2, BKS1, BKS2)"]
  end
  
  E --> I["Outcome:<br>Reliable price forecasts<br>for economic planning"]

Cited References (24)

1. HiSTGNN: Hierarchical spatio-temporal graph neural network for weather forecasting (2023) — link
2. Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system (2023) — link
3. Forecasting pine sawtimber stumpage prices: A comparison between a time series hybrid model and an artificial neural network (2023) — link
4. ARIMA-AdaBoost hybrid approach for product quality prediction in advanced transformer manufacturing (2023) — link
5. Imputation accuracy from low to medium-density SNP chips for US crossbred dairy cattle. (2023) — link
6. Comparison of imputation methods for missing production data of dairy cattle. (2023) — link
7. Robust imputation method with context-aware voting ensemble model for management of water-quality data. (2023) — link
8. Forecasting of daily new lumpy skin disease cases in Thailand at different stages of the epidemic using fuzzy logic time series, NNAR, and ARIMA methods. (2023) — link

0 of 24 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.