The Application of Time Series Analysis and All-around PCA in the Real Estate Cycle Fluctuations
Bibliographic record
Abstract
In the existing study on the real estate cycle fluctuations,the research tools used by the majority are not in depth portrait of the laws of Chian's real estate cycle fluctuations and its principal components which impacting most. To this end,we use the Time Series Analysis and All-around PCA to study China's real estate cycle fluctuations and its principal component since 1998 to 2008,. We found that,since the fourth quarter of 1998 to the third quarters of 2008,China's real estate growth cycle is probably gone through two complete cycles,they were 1th quarter of 1999 to the 4th quarter of 2003 ,1th quarter of 2004 to the 4th quarter of 2007,China's real estate has entered a downward adjustment in the process in 2008. From the view of All-around PCA,a greater impact on the real estate growth factors are:the growth rate of completed development investment,the growth rate of the construction area,the growth rate of vacant area,the growth rate of per capita income levels,GDP growth rate ,the growth rateof consumption sale's price,the growth rate of construction loan ,the growth rate of M2. And,the growth rate of other factors have smaller effects.
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.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".