Top Tweets for #SKTIME
Day13–16 of #100DaysOfCode 💻
Revising AI concepts for my mid-sems 🤖📚 and continuing work on my ongoing PR in the sktime repo 🚀
Balancing academics & open-source — step by step 💪
#AI #OpenSource #sktime #CodingJourney
🚀 Days 10–12 of #100DaysOfCode
✅ Completed half of the hard Array problems from Striver’s DSA Sheet
💻 Made 2 PRs in the sktime repo — issues #3429 & #8896
Loving the balance between DSA grind & open-source learning! 🌱
#Python #OpenSource #DSA #StriversSheet #sktime
Conformal Prediction for time series is 🔥🔥🔥🔥🔥🚀🚀🚀🚀.
At PyData New York conference whilst presenter of @sktime_toolbox gets a question 🙋♀️ about conformal prediction from the audience (36:44 onwards), confirms #conformalprediction has been integrated into #sktime.
The story about conformal prediction in open source time series libraries actually started with sktime, less than 2 years ago I run a poll asking which time series library will implement conformal prediction first and @FranzKiraly has risen to the challenge.
The rest is history, #sktime became the first open source library to implement the best uncertainty quantification framework for time series.
https://t.co/CTBwVDk7cJ
#timeseries #conformalprediction #forecasting
Today, i did time series and realized different #sktime forecasting strategies have their pros and cons. I did a cross-validation to determine the most 'accurate' #forecasting strategy; the one with least mean absolute error (MAE) value. @streamlit @numpy_team @sktime_toolbox
Welcome to sktime, the open community and Python framework for all things time series, such as forecasting, classification, and transformations with a familiar and user-friendly scikit-learn-like API. #DataScience #sktime #Python https://t.co/5RTnRRLZxs
Python ライブラリー「sktime」で学ぶ
らくらくビジネス時系列機械学習 Web講座
– 第7回：データの読み込み方・作り方（tsフォーマット編） –
#sktime
https://t.co/y2YC6qC1Am
Python ライブラリー「sktime」で学ぶ らくらくビジネス時系列機械学習 Web講座
– 第4回：時系列クラスタリング –
#sktime #Python時系列 #Python時系列解析 #時系列解析
https://t.co/IFPh2saLlK
Python ライブラリー「sktime」で学ぶ らくらくビジネス時系列機械学習 Web講座
– 第3回：時系列予測 –
#sktime #Python時系列 #Python時系列解析 #時系列解析
https://t.co/71flEmEiGt
⭐ DYK: The #pycaret #timeseries module provides a unified platform to consistently compare models from #statsmodels, #pmdarima, #sklearn, #prophet, etc with a few lines of code.
👉 More here: https://t.co/EPAHjAqRRE
🚀 Docs: https://t.co/Za7c8Njczz
#opensource #sktime #automl
💥 Did You Know 💥 You can add your own @sktime_toolbox compatible models/pipelines to the #pycaret #timeseries module!
👉 More on this here: https://t.co/iwPHbnOkMo
#opensource #machinelearning #datascience #sktime #timeseries #forecasting #analytics #automl
💥 Myth Buster 💥 Did you know that you can use low code libraries (like #pycaret) to get a better understanding of how models work and how they get built?
🚀 More on this here: https://t.co/9dYG4GpZtr
#opensource #machinelearning #datascience #sktime #forecasting #timeseries
You can access the book's entire code repository (#python) here: https://t.co/VN7KjPqRiu
#statsmodels, pandas, NumPy, SciPy, Scikit-Learn, #Sktime, #PyOD, Facebook Prophet, #NeuralProphet, #tensorflow , #pytorch , Darts , #pycaret
#timeseries
⭐ DYK: The latest release of PyCaret (3.0 RC) now supports #probabilistic forecasting. Users can customize point forecasts & prediction intervals to suit their business needs.
👉 Notebook: https://t.co/9OP6YfRzEh
#opensource #machinelearning #datascience #sktime #analytics
❓ How do you forecast when your timeseries is strictly positive but trending downwards? Negative forecasts can impact your metrics in this case.
💡 The upcoming release of the PyCaret will allow you to do just that.
🚀 https://t.co/QczkCnf9Pp
#opensource #forecasting #sktime
❓ How do you forecast when your time series has missing values? Most time series models do not work with missing data.
💡 The upcoming release of the PyCaret #timeseries module will allow you to do just that.
🚀 Code: https://t.co/wltudsCYt6
#opensource #forecasting #sktime
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