First of all I would suggest the library TA-Lib (Python interface to the C++ implementation)

https://github.com/mrjbq7/ta-lib

```
BBANDS Bollinger Bands
DEMA Double Exponential Moving Average
EMA Exponential Moving Average
HT_TRENDLINE Hilbert Transform - Instantaneous Trendline
KAMA Kaufman Adaptive Moving Average
MA Moving average
MAMA MESA Adaptive Moving Average
MAVP Moving average with variable period
MIDPOINT MidPoint over period
MIDPRICE Midpoint Price over period
SAR Parabolic SAR
SAREXT Parabolic SAR - Extended
SMA Simple Moving Average
T3 Triple Exponential Moving Average (T3)
TEMA Triple Exponential Moving Average
TRIMA Triangular Moving Average
WMA Weighted Moving Average
```

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Introductory to Middle level:

Moving average smoothing (introduction with examples in Python):

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How to Create an ARIMA (Autoregressive Integrated Moving Average ) Model for Time Series Forecasting with Python:

How to Create an ARIMA Model for Time Series Forecasting with Python

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Very useful and complete explanation about ARIMA and its variations /theory and applications/:

https://people.duke.edu/~rnau/411sdif.htm

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Kalman Filter:

An introduction to smoothing time series in python. Part III: Kalman Filter

and the corresponding implementation:

https://github.com/tmramalho/smallParticleFilter/blob/master/kalmanFilter.py

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Fourier decomposition (FFT, STFT), Hilber filter and others (standard python library, very useful!)

https://docs.scipy.org/doc/scipy/reference/tutorial/signal.html

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Time Series Prediction with LSTM (Keras)

Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras

==================================================================

Some more advanced topics:

Dynamic Time Warping

http://mlpy.sourceforge.net/docs/3.4/dtw.html

Global alignment and triangular global alignment kernels (implementation in C provided in the web site):

http://marcocuturi.net/GA.html