Comparative Study among ARIMA, SARIMA & XGBoost for Prediction of NIFTY IT Index
Bibliographic record
Abstract
Nifty IT index of India stock market is one of the most important yet neglected index when it comes to research and prediction. Prediction of Nifty IT index has benefits would be able to provide foresight and informed decision making to investors, traders, policy makers, etc. as Nifty IT represents IT sector of India. This research is a comparative study between three time series prediction algorithms viz. ARIMA, SARIMA and XGBoost for the most precise forecasting of Nifty IT index. The dataset used for this study has the Nifty IT index data of last 6 years. This time frame covers the dramatic historic moments such as covid-19 pandemic, Russia-Ukraine war, India-Canada tensions and the drastic changes in prices during these events. Three models were hyper parameter tuned and then compared on the basis of three metrices- MSE, RMSE and MAE. Out of the three, SARIMA models seem to have outperformed both ARIMA and XGBoost and hence the conclusion of the study is SARIMA is the most precise algorithm to use for prediction of Nifty IT index out of three.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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; both teacher heads agree on what is shown here.
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".