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REVIEW 5 major objections 5 minor 24 references

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

T0 review · 5 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper claims that daily rice prices in Banda Aceh after Covid-19 follow an ARIMA(0,0,5) model and forecasts a five-day drop followed by flat prices through December 2023.

desk verdict A routine ARIMA application undercut by non-invertible models and missing model-selection evidence; the flat forecast is a mathematical artifact, not a finding. read the letter →

arxiv 2411.15228 v1 pith:6B5TGM3B submitted 2024-11-21 q-fin.ST

Fadhlul Mubarak , Vinny Yuliani Sundara , Nurniswah This is my paper · ORCID
classification q-fin.ST MSC 91B8462-04
keywords MissingvalueimputationLOCFauto-ARIMAricequalityBandaAcehpriceforecastingARIMA
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that for six qualities of rice sold in Banda Aceh's traditional market, a single model order—ARIMA(0,0,5)—best describes the daily price series after Covid-19, and that the fitted models forecast a short decline in early September 2023 and then constant prices until the end of the year. The claim matters because stable rice prices are a food-security concern in Indonesia, and a simple model that can be refitted as new data arrive would give market monitors a cheap forecasting tool. The paper arrives at this by filling missing daily prices with the last observed value (LOCF) and then letting auto-ARIMA choose the order by AIC/AICC/BIC. If true, the six reported equations would be the working forecasting equations for these qualities.

What carries the argument

The machinery is the ARIMA(0,0,5) model—a moving-average model of order five, written as $X_i = c + \varepsilon_i + \theta_1\varepsilon_{i-1} + \cdots + \theta_5\varepsilon_{i-5}$, where $X_i$ is the daily price, $c$ the constant, and $\varepsilon$ the error. Because $d=0$, the model assumes the price series is already stationary and mean-reverting around a constant, with no trend or seasonality. The auto-ARIMA routine selects this order for all six qualities based on the AIC/AICC/BIC criteria, and the equations carry the forecast: the moving-average terms die out after five days, leaving the constant as the flat long-run prediction.

What would settle it

Compare the model's flat forecasts with actual daily rice prices for Banda Aceh from September 7 through December 31, 2023; any sustained upward or downward movement would contradict the claim. Running an Augmented Dickey-Fuller test on the imputed series would also check whether the d=0 (no differencing) choice is justified.

Watch

Extended reading notes

Core claim

The central discovery is that all six rice price series—lower quality I and II, medium quality I and II, super quality I and II—are best modeled as a pure moving-average process of order five, ARIMA(0,0,5), with no autoregressive terms and no differencing. Fitted to daily prices from the Banda Aceh traditional market between December 31, 2019 and August 31, 2023, the models produce forecasts that decline for the first six days of September 2023 and then become constant from September 7 through December 31, 2023. The paper reports the six fitted equations (7)–(12), one per quality, and uses the constants of those equations as the long-run flat forecast levels.

Load-bearing premise

The argument assumes that rice prices in Banda Aceh are stationary around a fixed average, with no trend or seasonal pattern, so a pure moving-average model with zero differencing captures the data, and this assumption is asserted rather than demonstrated with stationarity tests.

Editorial extensions

If this is right

  • If the ARIMA(0,0,5) model is right, price shocks in Banda Aceh's rice market fade within five days, after which the series returns to a constant level.
  • The forecast flat levels (e.g., 9995 IDR for lower quality I, 10423 IDR for lower quality II) are the model's predicted steady-state prices from September 7 to December 31, 2023.
  • The brief September 1–6 decline implies a one-time correction from August price levels, not a continuing trend.
  • Because the same model order fits all six qualities, the market appears to move together, so forecasts for one quality can cross-check the others.
  • The LOCF imputation creates a complete daily panel, which is what makes daily forecasts with no gaps possible.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The d=0 assumption is never tested with stationarity diagnostics; if the series are actually trending or seasonal, a differenced or seasonal ARIMA would likely give different, non-flat forecasts.
  • The perfectly flat forecast after five days is a structural feature of the pure moving-average model, not an empirical prediction that prices will literally stop moving; real market data typically fluctuate.
  • Applying the same auto-ARIMA pipeline to realized prices from September–December 2023 would provide a direct out-of-sample check on whether the constant forecasts held.
  • The same five-day memory could be tested on other staples in Banda Aceh (e.g., cooking oil, sugar) to see whether the short-memory pattern is specific to rice or a general market feature.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper analyzes daily rice prices for six qualities in Banda Aceh from December 31, 2019 to August 31, 2023, applies last-observation-carried-forward imputation to missing values, and uses auto-ARIMA to select forecasting models. The authors claim that ARIMA(0,0,5) is the best model for all six series, present fitted equations with MA coefficients, and forecast a decline from September 1 to September 6, 2023 followed by constant prices through December 31, 2023. The manuscript also includes residual plots and forecast tables for the four-month horizon.

Significance. If fully supported, the paper would offer a concrete empirical forecasting result for a staple-food price series in a region where such studies are scarce. The authors' use of LOCF imputation and auto-ARIMA is methodologically conventional, and they do make an explicit, falsifiable forecast for a defined horizon. However, the central claims are not substantiated by the evidence presented: model selection criteria are never reported, the stationarity assumption is never tested, the fitted MA polynomials are implausible, and the headline 'decline then constant' pattern is a mathematical consequence of the chosen model rather than an empirical discovery. The paper therefore does not currently establish a reliable forecasting model or a substantive finding about rice prices.

major comments (5)
  1. [Section III, below Eqs. (7)-(12)] The claim that ARIMA(0,0,5) is the 'best model' is unsupported because no AIC, AICC, or BIC values are reported anywhere in the manuscript. The abstract states that auto-ARIMA selects the best model based on one of these criteria, and Eqs. (4)-(6) define them, but no numerical values or comparisons among candidate models are given. Without this information, the reader cannot verify that ARIMA(0,0,5) outperforms other orders or that the choice is data-driven.
  2. [Section III, Eqs. (7)-(12)] The fitted MA(5) models are not credible as stationary invertible representations of the data. For example, BKB1 in Eq. (7) has MA coefficients 2.08, 2.74, 2.52, 1.56, and 0.58, several of which exceed 1 in absolute value; similar patterns appear in all six equations. While individual coefficients greater than 1 do not automatically violate invertibility, the manuscript provides no check of the roots of the MA polynomial, and a standard auto-ARIMA routine would not normally return a non-invertible model. The reported coefficients suggest that the model is absorbing nonstationarity or level shifts through a non-invertible moving-average component, which undermines the interpretation of the equations as valid forecasting models.
  3. [Section III, pre-Eq. (7) and Tables 3-4] The load-bearing premise that each series is stationary in levels (d=0) is never tested. The paper reports no Augmented Dickey-Fuller test, KPSS test, or any other stationarity diagnostic. The data shown in Tables 1 and 3 themselves contradict this premise: BKB1 moves from 9850 IDR in early 2020 to 11600 IDR in August 2023, a clear long-run level shift, and similar shifts appear in the other series. If any of the six series contains a unit root or a trend, then ARIMA(0,0,5) in levels is misspecified and all forecasts in Tables 5-8 are invalid.
  4. [Tables 5-8 and Figs. 7-12] The headline result that prices decline from September 1 to September 6 and then remain constant is a mathematical property of the fitted MA(5) model, not an empirical finding. For an MA(5) with a constant and no differencing, the h-step-ahead forecast converges to the constant after h=5, so the constant values repeated throughout Tables 6-8 are simply the estimated intercepts from Eqs. (7)-(12). The paper does not draw this connection and instead presents the plateau as a substantive forecast result, which is circular.
  5. [Section III, Figs. 1-6] The residual diagnostics are inadequate to support the statement that 'the model is suitable for use in forecasting.' Figures 1-6 are plot images with no accompanying test statistics such as the Ljung-Box Q statistic, no residual ACF/PACF tables, and no numerical white-noise test results. Visual inspection of plots without objective criteria is not sufficient evidence of adequately modeled residuals, especially given the other specification concerns.
minor comments (5)
  1. [Section II, Eq. (1)] The notation in Eq. (1) is inconsistent: the text describes A_i-e as a recursive lag search, but the equation uses A_{i-a} without defining a; the manuscript should align notation and clarify the imputation rule.
  2. [Section II, text after Eq. (3)] The phrase 'c is konstanta' mixes Indonesian and English; it should read 'c is a constant.' Similar language issues appear elsewhere (e.g., 'Tabel' for 'Table' in Tables 6 and 8).
  3. [Section III, Eqs. (7)-(12) and Fig. 6] Figure 6 is captioned 'Residuals testing from BKS1 variable,' but Eq. (12) and the surrounding text indicate it should be for BKS2; the label should be corrected.
  4. [Section III, text around Tables 5 and 6] Several numerical inconsistencies appear between the text and tables: the text reports BKS1 September 4 as 12358 IDR and September 5 as 12419 IDR while Table 5 shows 12359 and 12420; for BKS2 the text lists an extra '11704 IDR' and gives a seven-value sequence for a six-day period. These discrepancies should be reconciled.
  5. [Section IV, Conclusions] The conclusion states that the super quality rice II forecast range is 'between 11388 IDR and 12761 IDR' but refers to it as 'super quality rice I,' repeating the earlier mislabeling; this should be corrected.

Circularity Check

1 steps flagged · score 6.0 of 10

The 'decline then constant' forecast is a built-in property of the fitted MA(5) models: every horizon beyond 5 steps equals the fitted constant, so Tables 5-8 restate estimated parameters rather than independent predictions.

  1. fitted input called prediction [Abstract; Section III 'Results and Analysis', Equations (7)-(12) and Tables 5-8; Section IV Conclusions]
    "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)."

    For a stationary ARIMA(0,0,5) process Y_t = c + ε_t + θ1ε_{t-1}+...+θ5ε_{t-5}, the h-step forecast for h>5 is exactly the fitted constant c, because all future innovations have conditional expectation zero. The paper's own equations (7)-(12) give constants c = 9995.01, 10422.90, 10642.36, 10678.66, 12313.12, 11387.50, and Tables 5-8 list 9995, 10423, 10642, 10679, 12313, 11388 for every day from September 7 to December 31. Thus the headline 'remained constant' is not an empirical forecast of future prices; it is a mathematical consequence of the chosen MA(5) structure and is numerically identical to the estimated constant terms. The fitted parameter is renamed as a prediction.

full rationale

The paper's central model-selection claim (ARIMA(0,0,5) is best by AIC/AICC/BIC) is not itself circular: it is an empirical model-selection statement, though it is unsupported because no AIC/AICC/BIC tables, stationarity tests, or Ljung-Box diagnostics are shown. The main circularity is in the forecasting conclusion. Equations (7)-(12) define pure MA(5) models in levels. For any such model, forecasts beyond five steps collapse to the constant term. The paper then presents this collapse as a discovery: prices decline from September 1 to September 6, 2023 and 'remained constant' from September 6 to December 31, 2023. Tables 5-8 repeat the fitted constants to the nearest integer for over three months. This is the fitted-input-called-prediction pattern: the estimated constant is relabeled as a multi-month forecast. The decline portion also comes from the same fitted MA recursion, so the entire forecast path is a restatement of the estimated model parameters rather than an independent out-of-sample prediction. There are no load-bearing self-citations and no renamed known result, and the missing stationarity/invertibility evidence is better classified as a correctness risk than as circularity. The score is 6 because one central conclusion (constant future prices) reduces by construction to fitted values, while the model-selection claim retains independent empirical content if properly verified.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

All model parameters are fitted prices, not derived from theory. The central forecast result is a direct consequence of the MA(5) form. The paper introduces no new entities.

free parameters (6)
  • BKB1 ARIMA(0,0,5): constant and theta_1..theta_5 = 9995.01; 2.08, 2.74, 2.52, 1.56, 0.58
    Fitted to training data by auto-ARIMA; no standard errors or AIC values reported.
  • BKB2 ARIMA(0,0,5): constant and theta_1..theta_5 = 10422.90; 1.73, 2.11, 1.97, 1.37, 0.62
    Fitted to training data by auto-ARIMA; no standard errors or AIC values reported.
  • BKM1 ARIMA(0,0,5): constant and theta_1..theta_5 = 10642.36; 1.84, 2.39, 2.13, 1.34, 0.52
    Fitted to training data by auto-ARIMA; no standard errors or AIC values reported.
  • BKM2 ARIMA(0,0,5): constant and theta_1..theta_5 = 10678.66; 1.83, 2.33, 2.10, 1.35, 0.52
    Fitted to training data by auto-ARIMA; no standard errors or AIC values reported.
  • BKS1 ARIMA(0,0,5): constant and theta_1..theta_5 = 12313.12; 1.63, 1.95, 1.79, 1.29, 0.60
    Fitted to training data by auto-ARIMA; no standard errors or AIC values reported.
  • BKS2 ARIMA(0,0,5): constant and theta_1..theta_5 = 11387.50; 2.08, 2.79, 2.54, 1.59, 0.60
    Fitted to training data by auto-ARIMA; no standard errors or AIC values reported.
assumptions (5)
  • standard math ARIMA/MA(q) time series theory: MA(q) processes are stationary and h-step forecasts converge to the unconditional mean after q steps.
    Used to write Equations 2-3 and to produce Tables 5-8.
  • domain assumption Each of the six rice price series is stationary around a constant mean with no trend, seasonality, or structural breaks, so d=0 is appropriate.
    The paper selects ARIMA(0,0,5) without reporting stationarity tests or considering differencing in the final models.
  • domain assumption Missing prices are missing at random in a way that LOCF imputation preserves the price process.
    LOCF is applied in Equation 1 without diagnostic checks; missing dates are mostly weekends and holidays, where carrying forward the previous weekday price may not reflect actual market prices.
  • domain assumption auto-ARIMA's automated selection across candidate models is correct and complete, and the chosen model is genuinely best by AIC, AICC, or BIC.
    No selection criteria values or candidate model lists are reported; the 'best' label is taken on faith.
  • ad hoc to paper Residuals of the fitted models are white noise, so the models are adequate for forecasting.
    The paper concludes suitability from Figures 1-6 but provides no test statistics; given non-invertible MA coefficients, this is questionable.

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Cite this review

Pith. "Pith review of Forecasting the Price of Rice in Banda Aceh after Covid-19." pith.science (2026). https://pith.science/paper/6B5TGM3B

@misc{pith2026241115228,
  author       = {Pith},
  title        = {Pith review of: Forecasting the Price of Rice in Banda Aceh after Covid-19},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6B5TGM3B}},
  note         = {Machine review of arXiv:2411.15228}
}
read the original 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).

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Reviewed August 12, 2026 · model on record in the stance chip above.