DOI: 10.5281/zenodo.17256230
title: Radar Data Tables

description: Scope and purpose

These tables provide the inputs and normalized scores used to generate the normalized radar charts that compare methods M1, M2, M3, Proposed across datasets (Corn (control), Chinese Kale, Rock Melon, Tomato Ambient, Tomato S1, Tomato S2, Tomato S3–S6, Tomato S7). They enable third parties to reproduce the figures and verify trends without access to raw time-series.

Files

Radar_Raw.csv — raw (unnormalized) metrics per dataset × method.

Radar_Normalized.csv — per-dataset 0–1 scores for radar axes (higher = better).

Table_B_Efficiency.csv — runtime/resource context (referenced by efficiency figures).

File schema

1) Radar_Raw.csv




Column
Meaning
Units/Notes




Dataset
Dataset name (e.g., “Tomato S3–S6”)
String


Method
One of {M1, M2, M3, Proposed}
String


MAPE
Mean Absolute Percentage Error
% (numeric)


RMSE
Root Mean Squared Error
metric units of variable; numeric


VarReduction_pct
Variance reduction vs. raw
% (higher is better)


OutlierSuppression_pct
Suppressed outliers vs. raw
% (higher is better)


Latency_seconds
Processing time per sample/segment (see paper)
seconds; numeric


Estimated
“No” if value is reported explicitly in the manuscript; “Yes” if logically estimated to avoid blanks in comparisons
String {Yes, No}






Notes: The file preserves unusual values as reported (e.g., negative RMSE printed for one case), to reflect the manuscript faithfully.


2) Radar_Normalized.csv




Column
Meaning
Notes




Dataset
Dataset name
String


Method
{M1, M2, M3, Proposed}
String


Accuracy_from_RMSE
Normalized score (higher = better) from RMSE
RMSE inverted + min–max normalized per dataset


Error_from_MAPE
Normalized score (higher = better) from MAPE
MAPE inverted + min–max normalized per dataset


Robustness
Normalized OutlierSuppression_pct
min–max normalized per dataset


Smoothness
Normalized VarReduction_pct
min–max normalized per dataset


Latency
Normalized (higher = better) from latency
Latency inverted + min–max normalized per dataset




Normalization (per dataset)

To make all axes “higher = better,” we apply min–max scaling within each dataset:





For metrics where higher is better (Robustness, Smoothness):score = (x − min) / (max − min)




For metrics where lower is better (RMSE, MAPE, Latency):score = (max − x) / (max − min)



This yields values in [0, 1] for each axis and avoids cross-dataset scale confounds. The radar axes are then:





Accuracy_from_RMSE (from RMSE, inverted)




Error_from_MAPE (from MAPE, inverted)




Robustness (OutlierSuppression_pct)




Smoothness (VarReduction_pct)




Latency (from Latency_seconds, inverted)





Reproducing the radar charts





Filter Radar_Normalized.csv to one Dataset.




Pivot to a 5×4 grid (rows = axes above, columns = methods).




Plot a radar/spider chart with values in [0,1]. (Any software is fine: Python/matplotlib, R, Excel.)




Optional: annotate with the corresponding raw values by joining to Radar_Raw.csv for context.





Reproducibility & provenance





Any cell marked Estimated = “Yes” in Radar_Raw.csv is a logical estimate added solely to avoid blanks and to enable like-for-like plotting across all methods/datasets. Exact values from the manuscript are marked Estimated = “No”.




Radar_Normalized.csv is derived from Radar_Raw.csv using the rules above; no further smoothing or weighting is applied.




Efficiency summaries used in the paper’s runtime figures are provided in Table_B_Efficiency.csv for transparency.





License & citation (suggested)





License: CC BY 4.0 (or your journal’s required license).




How to cite (example):Supplementary Radar Data Tables for “Advancing Agricultural Time-Series Data Analysis,” Zenodo, DOI: <10.5281/zenodo.17256230>.





Quick sanity checks (recommended)





Within each dataset, normalized values should lie in [0, 1] and at least one method should attain 0 and one 1 on each axis.




Visual ranking on the radar should be consistent with the RMSE/MAPE directionality and the Latency advantage patterns discussed in Section F.