Modeling Trend in Telecommunication in Sri Lanka: A Case study on Internet and Cellular Connections
Bibliographic record
Abstract
The telecommunication is one of the modes of communication, in which most investments are made. It consists of internet, mobile phones, wired and wireless fixed phones, fax, televisions, radio and some other. Among them, demand for internet and cellular phones rapidly increases. For a smooth function of this business, knowledge on demand is much important. Effective forecasts help a business to manage its supply efficiently. This study aimed to find out an accurate mechanism for prediction of demand for internet conections and cellualr phone collections.Based on the secondary data available in central bank reports from 1996 to 2016, several statistical forecasting models were evaluated for an accurate prediction. There can be seen an increasing demand for both internet and cellular phone connections. Number of internet connections has gone up from 4 110 to 4 921 000, while the usage of cellular phones has developed from 71 228 to 26 228 000 during this period. Rapid growth in internet usage has happened after 2009, while after year 2003, usage of cellular phone has increased rapidly. With compared to models fitted for original form of data, models for log transformed data show better performances. The best performance in prediction of internet connection was given by ARIMA (1,1,1) model fitted for log transformed data, meanwhile ARIMA (0,1,2) model fitted for log transformed data showed the best fit for series of cellular connections. Double exponential smoothing models also show better fit for both series.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".