Venkata Duvvuri ¹ , Nikhil Reddy Thokala ¹ , Jaikalyan Tatineni ¹ , Abhishek Srinivas ² and Priya Ganesh Phapale ¹ , ¹ USA, ² Northeastern University & Trine University, USA

Within the broader context of a Vedic horoscope, VedicVisions.ai is an AI-based two-stage Vedic forecasting system combining deterministic Vedic calculations with generative AI. Natal-chart factors, planetary transits, dashas, houses, Nakshatra, and forecast scores provide the chart and timing foundation; generative AI converts these calculated inputs into a personalized six-month narrative and context-aware guidance for career, finances, health, relationships, and family. The AI narrative is fetched in the background, displayed in the application, and incorporated into the downloadable PDF report alongside the deterministic astrology calculations. Stage 2 provides granular daily Tarabalam classifications – Excellent, Good, Caution, Unfavorable, or Avoid – based on the relationship between the user’s Janma Nakshatra and the daily Nakshatra. This study evaluates the system using 161 daily observations. Mood assessment was blinded to the day’s Tarabalam classification: mood was recorded before the participant viewed that day’s label. Positive mood occurred on 77.6% of observed days. Good days were positive in 43/55 observations (78.2%, p < .001 versus a 50% reference), Excellent days in 28/40 (70.0%, p = .008), and Good/Excellent days together in 71/95 (74.7%). These favorable-day results show substantial absolute positive-emotional experience and are directionally consistent with the traditional preference for auspicious timing, although they did not significantly exceed the subject’s already-high 77.6% period base rate. Caution, Unfavorable, and Avoid labels matched negative/meh mood in 22.2%, 18.2%, and 11.8% of observations, respectively; Avoid days were positive in 15/17 observations (88.2%), and the overall ordinal association was small and nonsignificant (Spearman rho = -0.12, p = 0.132). The broader VedicVisions.ai forecasts characterized much of 2025-2026 as Mostly favorable, with displayed scores around 6.4-6.7/10, providing context for the high overall positive-mood rate. Together, the findings provide encouraging preliminary observational evidence for the layered framework: Stage 1 supplies AI-enriched period-level forecast context, while Stage 2 offers a granular timing layer whose incremental predictive value requires prospective validation. The findings are consistent with the traditional practice of referring to a favorable day in advance; however, the observational design cannot establish causation, and prospective multi-user studies are needed to establish generalizability, domainspecific outcomes, and incremental predictive value.

Tarabalam, Vedic astrology, Nakshatra, VedicVisions.ai, generative AI, mood, ecological momentary assessment, hierarchical forecasting, positive outcomes.