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maxmc

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Karma
230
Created
February 17, 2022 (4y ago)
Submissions
0
YC Badge: 0xff7e05ce05954e57b9d26614d6b3fcd96a89f7cf

YC Badge: 0xff7e05ce05954e57b9d26614d6b3fcd96a89f7cf

  1. Open Source FinOps GPT: Using GPT-4, TimeGPT and Vantage (github.com)
    8 points 0
  2. Statistical methods outperform BigQuery's Auto ML (github.com)
    4 points 0
  3. Statistical methods outperform Amazon’s ML Forecast (github.com)
    3 points 1
  4. Statistical vs. Deep Learning forecasting methods (github.com)
    222 points 84
  5. Simple Seasonal Naive: faster and more accurate than Prophet and NeuralProphet (github.com)
    1 points 1
  6. Show HN: Probabilistic hierarchical forecasting with statistical methods (github.com)
    2 points 0
  7. Probabilistic hierarchical reconciliation for time series (github.com)
    3 points 0
  8. Trends and Opportunities in Time Series (gradientflow.com)
    1 points 0
  9. Complex Exponential Smoothing (CES) is now available in Pyhton (github.com)
    2 points 0
  10. Python implementations of time series forecasting and anomaly detection (robjhyndman.com)
    2 points 0
  11. Show HN: Scalable Time Series Modeling with open-source projects (nixtla.github.io)
    5 points 1
  12. 1 points 0
  13. Exponential Smoothing: faster and more accurate than NeuralProphet (github.com)

    We benchmarked on more than 55K series and show that ETS improves MAPE and sMAPE forecast accuracy by 32% and 19%, respectively, with 104x less computational time over NeuralProphet. We hope this exercise helps the…

    161 points 43
  14. Exponential Smoothing (ETS) for Python (github.com)
    1 points 1
  15. Show HN: Fast and robust Auto Arima in Python (github.com)
    2 points 0