Seasonal Trends and Consumer Behavior in Nintendo Game Sales: Analysis and Forecasts for 2025-2026
Bibliographic record
Abstract
As the approaching of feeling economy, video games have becoming one of the key medium of psychological and social engagement. The study mainly contributes to find out the relationship between the sales of different types of games and seasons and the motivations behind the trends, and do the forecasting for future trend, while also providing suggetion for sales strategies. The paper uses sales data, during 2022 to 2024, from Nintendo stores in Zhejiang Province( 47, 962 records). The research uses basic Excel tools and Python to organizing data and Error-Trend-Seasonal model (ETS) in R Studio to predict the future seasonal trends. The result draws that there is the relatioship between game type sales and seasonal changes. Consumers preferences are shifting as the seasons change. In addition, I also find out there usually is a peak in first quarter (Q1), with action, action-adventure, and adventure games remaining dominant.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".