Prediction of CPI in Saudi Arabia: Holt’s Linear Trend Approach
Bibliographic record
Abstract
The Consumer Price Index (CPI) indicates and measures price level changes in an economy based on total purchased goods and services. CPI is calculated by dividing the cost of the market basket in a given year by the cost of the market basket in a Base Year and multiplied by 100. CPI is subject to the Classification of Individual Consumption by Purpose (COICOP) in Saudi Arabia. Home expenditure items, such as housing, water, electricity, gas, and other fuels, food, and beverages are considered high while weight, health, education, recreation, and culture are given low weight in the COICOP in Saudi Arabia. Holt’s linear model based on three equations and popularly known as double exponential smoothing or linear exponential model and the most commonly used method in forecasting data based on internal trends. Holt’s linear model is composed of three equations relating to smoothing, trend, and forecast. In this paper, the CPI data were taken monthly from the General Authority for Statistics, Saudi Arabia for the period from May 2019 to July 2020 with 15 realizations. The CPI for Saudi Arabia is predicted for the next twelve months and as observed from the trend of CPI, the prices of total purchased goods will increase in the next eleven months.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".