Time Series Analysis of Netflix's Stock Closing Prices: From Data Processing to Forecasting
Bibliographic record
Abstract
This study delves into the complexities of time series forecasting, a field that intersects statistics, data science, and econometrics to predict sequential data patterns.Focusing on the challenge of forecasting Netflix's closing stock prices from February 2018 to January 2022, we evaluated the performance of three distinct models: SARIMA (seasonal autoregressive integrated moving average), Prophet, and XGBoost (extreme gradient boosting).Each model demonstrated unique strengths and limitations.SARIMA offered solid baseline accuracy but struggled to capture abrupt price fluctuations inherent in stock market behavior.Prophet enhanced interpretability by effectively modeling seasonality and trends, yet it showed limitations in precision.In contrast, XGBoost excelled in capturing complex nonlinear patterns and better reflected the dynamic nature of stock price movements.The core innovation of this research lies in the development of a hybrid model combining SARIMA and XGBoost through optimized weighting.This hybrid approach outperformed individual models by balancing statistical robustness with adaptive learning capabilities, leading to improved accuracy and better trend representation.However, while Root Mean Square Error (RMSE) was used as the primary evaluation metric, it became evident that RMSE alone is insufficient to fully assess forecasting quality, particularly in capturing trend dynamics.This highlights the necessity for more comprehensive evaluation metrics, paving the way for future research in advancing time series forecasting methodologies.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.002 | 0.001 |
| 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".