Time Series Analysis of Product Demand Forecasting and Inventory Optimization on E-commerce Platforms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the continuous advancement of reform and opening up, China's economy has welcomed rapid development, and e-commerce platforms have sprung up like bamboo shoots after rain. The purpose of this study is to use time series models to forecast demand and optimize inventory for thousands of merchants, goods, and supporting warehouses on the e-commerce platform. First, an ARIMA time series model is established for the shipment of old products over time, and through continuous iteration, the optimal parameters of the time series model are obtained for predicting the old products. Then, using K-means clustering, the final prediction results are categorized. Later, new products replace the old ones, and after extracting the feature values of both new and old products to conduct cosine similarity analysis, adjustments are made to the new prediction model to obtain the final forecast values.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.015 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it