Forecasting

Enlaces, tutoriales

• Almost Everything You Need to Know About Time Series: Understand moving average, exponential smoothing, stationarity, autocorrelation, SARIMA, and more
  • End to End Time Series Analysis and Modelling: Apply moving average, exponential smoothing, and SARIMA for stock prediction
• 7 methods to perform Time Series forecasting (with Python codes)
• A Guide to Time Series Forecasting with ARIMA in Python 3
• Using Python and Auto ARIMA to Forecast Seasonal Time Series
• Seasonal ARIMA models
• pmdarima: Pyramid brings R's beloved auto.arima to Python, making an even stronger case for why you don't need R for data science. https://www.alkaline-ml.com/pmdarima. Pmdarima (originally pyramid-arima, for the anagram of 'py' + 'arima') is a no-nonsense statistical Python library with a solitary objective: bring R's auto.arima functionality to Python. Pmdarima operates by wrapping statsmodels.tsa.ARIMA and statsmodels.tsa.statespace.SARIMAX into one estimator class and creating a more user-friendly estimator interface for programmers familiar with scikit-learn.

Documentación, libros

• Forecasting: Principles and Practice: This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly.We use R throughout the book and we intend students to learn how to forecast with R.

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