Time Series

• Nested cross validation
• TS and feature selection
• Out-of-time validation
• scikit-learn prediction intervals
• Forecast visualization
• TS transfer learning
• Detecting stationarity
• End to end project
• Analysis and forecasting
• Seasonal ARIMA
• Forecasting models overview
• Causality inference
• Giotto time
• Time Series Analysis
• Time series in Python
• Matrix profile
• Brownian motion in Python
• Forecast energy consumption with neural networks and xgboost
• TS forecasting
• Statistical tests and ARIMA
• Dynamic time warping
• Whale identification TS processing
• Pattern mining with stumpy
• Statistical tests for trend
• How to synchronize time series
• Time series libraries
• Kats by Facebook
• Merlion by Salesforce
• Darts
• Avoid data leakage in time series
• Orbit by Uber for Bayesian time series forecasting
• Time Series terminology
• Time Series forecasting cheatsheet
• Poisson Hidden Markov Model for Time Series regression
• PyCaret AutoML for Time Series
• Seasonal adjustment of daily time series
• TSA basics
• Univariate time series forecasting with Neural Networks
• sktime as sklearn TSA interface
• Changepoints detection with e-divisive
• Time series data visualization
• statsforecast for lightning fast forecasting
• Time features encoding: cyclic vs dummy vs numeric
• Scalecast
• Hierarchical forecast reconciliation
• Deep Learning for time series forecasting
• Interpreting ACF and PACF plots for time series
• Python Automatic Forecasting
• Forecasting with tree-based algorithms
• FEDOT: an AutoML approach to time series forecasting
• Time series forecasting with Transformers
• Conformal prediction interval with scikit-learn, MAPIE and TSPIRAL
• mlforecast: scalable machine learning based time series forecasting
• Time Series Forecasting with Scikit-learn
• Time Series for Climate Change: Forecasting Energy Demand
• Skforecast: a Python library that eases using scikit-learn regressors as single and multi-step forecasters
• Time series complexity analysis using entropy
• functime is a powerful Python library for production-ready global forecasting and time-series feature extraction on large panel datasets
• Feature Engineering for Time Series Regression
• TimeGPT: The First Foundation Model for Time Series Forecasting
• Group time-series split
• Feature Engineering for Time Series
• TSMixer: The Latest Forecasting Model by Google
• tsfresh: Time Series Feature extraction based on scalable hypothesis tests
• pytimetk: time series easier, faster, more fun
• autogluon: AutoML for Image, Text, Time Series, and Tabular Data
• AutoGluon-TimeSeries: Every Time Series Forecasting Model In One Library
• Time Series Forecasting with TiDE

Prophet

• Is Facebook's "Prophet" the Time-Series Messiah, or Just a Very Naughty Boy?
• LSTM and Prophet
• Prophet forecasting
• AutoArima Prophet adapter in statsforecast
• Fixing Prophet forecasting issue

Greykite

• Greykite by Linkedin
• Intro to Greykite
• Tuning a model in Greykite