The purpose of this project is to demonstrate the application of three main forecasting functions: single exponential smoothing, double exponential smoothing and Holt-Winters forecasting.
Install Matplotlib with pip install matplotlib
Install NumPy with pip install numpy
Install PyShark with pip install pyshark
Install SciPy with pip install scipy
The project consists of a few Python files:
- APIForecast.py: here are implemented the three forecasting functions, along with SSE and RSI functions and two fitting functions. The fitting functions differs in the algorithm that they use: one uses the Nelder-Mead algorithm with some tweaks, and the other uses the TNC fitting algorithm from the SciPy package.
- Utils.py: this files contains the plotting functions, which uses matplotlib, and a couple of utility functions for output formatting.
- CreateDatasets.py: it implements the generation of the values datasets which we used for testing, with the options to create a "normal" dataset or an "anomalous" dataset. It also can produce a dataset from a pcap file.
- Three demo scripts:
- Demo.py: used to test the API by passing what we want to do via arguments on the CLI. It can read both json datasets and pcap files.
- DemoInteractive.py: and interactive version of the previous script.
- Test.py: an automatic test for Holt-Winters with default parameters.
First, create a dataset with python3 CreateDatasets.py --type series --days 5
Then, we can use Demo.py to do a forecasting demo: python3 Demo.py --dataset dataset.json --season 288 --rsi 24 --alpha 0.57300 --beta 0.00667 --gamma 0.92767
Or we can use Test.py to do another forecasting demo: python3 Test.py
