Forecasting Amazon’s Quarterly Net Sales Based on Time Series
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
This paper presents an in-depth analysis of the Autoregressive Integrated Moving Average (ARIMA) model for forecasting Amazon’s quarterly net sales, using historical data from 2007 to 2019. The model’s ability to handle trends and seasonality in time series data is highlighted. The study outlines the data transformation process, including log transformations and differencing, to ensure stationarity before model development. Four potential ARIMA models were constructed based on the observed autoregressive and moving average characteristics. The ARIMA(3,1,4) model was ultimately selected for its optimal balance between simplicity and prediction accuracy. A thorough diagnostic assessment was then conducted to ensure that the selected model met important assumptions of the ARIMA framework. The study proceeds to forecast Amazon’s sales for the next eight quarters in 2020 and 2021, demonstrating the model's practical utility in predicting future sales trends. The insights obtained aim to optimize inventory management, improve resource allocation, and better understand seasonal sales fluctuations. These findings offer strategic insights for e-commerce decision-makers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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