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I am working on time series in python. The libraries which I found useful and promising are
Also for visualization: matplotlib
Does anyone know a library for exponential smoothing?
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1) Tsfresh. The name of this library, Tsfresh, is based on the acronym “Time Series Feature Extraction Based on Scalable Hypothesis Tests.” It is a Python package that automatically calculates and extracts several time series features (additional information can be found here) for classification and regression tasks.
Pandas. Pandas (Python data analysis) is a must in the data science life cycle. It is the most popular and widely used Python library for data science, along with NumPy in matplotlib.
Box-Jenkins ARIMA models: These univariate models are used to better understand a single time-dependent variable, such as temperature over time, and to predict future data points of variables. These models work on the assumption that the data is stationary.
Pandas has exponentially weighted moving moment functions
http://pandas.pydata.org/pandas-docs/dev/computation.html?highlight=exponential#exponentially-weighted-moment-functions
By the way, there shouldn't be any functionality leftover in the scikits.timeseries package that is not also in pandas.
Edit: Since this is still a popular question, there is now a work in progress pull request to add more fully featured exponential smoothing to statsmodels here
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