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1.Benchmarking foundation models for time series (github.com/nixtla)
4 points by LevoMX on May 29, 2024 | past
2.TimeGPT: Production Ready Time Series Foundation Model for Forecasting (github.com/nixtla)
52 points by LevoMX on May 8, 2024 | past | 7 comments
3.Open Source FinOps GPT: Using GPT-4, TimeGPT and Vantage (github.com/serpa-cloud)
8 points by LevoMX on Aug 21, 2023 | past
4.Statistical methods outperform BigQuery's Auto ML (github.com/nixtla)
4 points by LevoMX on Feb 28, 2023 | past
5.Statistical methods outperform Amazon’s ML Forecast (github.com/nixtla)
3 points by LevoMX on Dec 12, 2022 | past | 1 comment
6.Statistical vs. Deep Learning forecasting methods (github.com/nixtla)
222 points by LevoMX on Dec 1, 2022 | past | 81 comments
7.Simple Seasonal Naive: faster and more accurate than Prophet and NeuralProphet (github.com/nixtla)
1 point by LevoMX on Nov 28, 2022 | past | 1 comment
8.Show HN: Probabilistic hierarchical forecasting with statistical methods (github.com/nixtla)
2 points by LevoMX on Nov 17, 2022 | past
9.Probabilistic hierarchical reconciliation for time series (github.com/nixtla)
3 points by LevoMX on Nov 15, 2022 | past
10.Trends and Opportunities in Time Series (gradientflow.com)
1 point by LevoMX on Nov 4, 2022 | past
11.Complex Exponential Smoothing (CES) is now available in Pyhton (github.com/nixtla)
2 points by LevoMX on Oct 10, 2022 | past
12.Python implementations of time series forecasting and anomaly detection (robjhyndman.com)
2 points by LevoMX on Oct 7, 2022 | past
13.Show HN: Scalable Time Series Modeling with open-source projects (nixtla.github.io)
5 points by LevoMX on Oct 6, 2022 | past | 1 comment
14.Exponential Smoothing: faster and more accurate than NeuralProphet (github.com/nixtla)
161 points by LevoMX on Aug 17, 2022 | past | 42 comments
15.Exponential Smoothing (ETS) for Python (github.com/nixtla)
1 point by LevoMX on July 19, 2022 | past | 1 comment
16.Show HN: Fast and robust Auto Arima in Python (github.com/nixtla)
2 points by LevoMX on Feb 21, 2022 | past

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