Time Series-Driven Demand Forecasting Framework for E-Commerce Inventory Management
Bibliographic record
Abstract
This paperproposes a Time Series-based Demand Forecasting Framework of E-Commerce Inventory Management, which will be able to increase the accuracy of the prediction as well as will be able to optimize the control of stock in dynamic online stores. Z-Score Normalization is used in the framework during preprocessing to remove skewness in data and keep the numbers stable. Recursive Feature Elimination can be used to eliminate redundant and less informative variables to enable the model to concentrate on important behaviors and temporal patterns. The main component of the forecasting model is a BiLSTM with Attention Mechanism which is implemented on top of the TensorFlow platform and it is useful in modeling the intricate sequential relationships and focuses on key time windows that affect demand. Experiment analyses indicate that the proposed system can save a lot of forecasting errors with low RMSE, MAE, and MAPE values in comparison with orthodox approaches. The findings prove the framework strong, flexible, and interpretable, which is a valuable decision in real-time inventory optimization and demand-based decision-making of e-commerce management systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".