ARIMA MODEL BUILDING AND FORECASTING ON IMPORTS AND EXPORTS OF PAKISTAN
Bibliographic record
Abstract
From the day one, mankind has always been interested in to the future. As the civilization advanced with growing sophistication in all phases of life, the need to look in to the future also grew with it. Today every government, public private organizations, as well as an individual would like to predict and plan for the future. In order to attain a better growth in the economy of a country, modeling and forecasting is the most important tool now a day, this can be done by one of the statistical technique called a Time series analysis. In this paper we tried to build a time series model called ARIMA (Auto Regressive Integrated Moving Average) model with particular reference of Box and Jenkins approach on annually total Imports and Exports of Pakistan from the year 1947 to the year 2013 with useful statistical software R. Validity of the fitted model is tested using standard statistical techniques. The fitted model is then use to forecast some future values of Imports and export of Pakistan. It is found that an ARIMA (2, 2, 2) and ARIMA (1, 2, 2) model looks suitable to forecast the annual Imports and Exports of Pakistan respectively. We also found an increasing trend both in case of Imports and Exports during this study.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".