Comparative Analysis of Sampling Frequency Effects on Forecasting Performance in Scalar and Functional Data: Autoregressive and LSTM Models
Bibliographic record
Abstract
This study systematically examines the influence of sampling frequency on forecasting performance in both scalar and functional time series, employing autoregressive (FAR) and long short-term memory (LSTM) models. Four diverse datasets—NOAA daily temperature, UCI household power consumption, monthly sunspot observations, and Canadian weather functional data—are analyzed across high, medium, and low sampling frequencies. Functional principal component analysis is applied to reduce the dimensionality of functional datasets. Results show that medium and low sampling frequencies often improve forecast accuracy by mitigating noise and emphasizing key temporal structures. Across most datasets and frequencies, LSTM consistently outperforms FAR, especially at lower sampling rates. Fourier spectral analysis reveals distinct frequency-dependent behaviors, highlighting the value of adaptive sampling strategies tailored to dataset-specific characteristics. These findings provide practical guidance for selecting optimal sampling frequencies in both scalar and functional time series forecasting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".