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timeseries-analysis

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ChadFulton
ChadFulton commented Sep 11, 2019

Collection of follow-ups to #5827. These can/should be broken out into individual PRs. Many are relatively straightforward and would make a good first PR.

General

  • Documentation (none was added in original PR).
  • Release notes.
  • Example notebook.
  • Double-check how sm.tsa.arima.ARIMA works with fix_params (it should fail except when the fit method is statespace
grass
wenzeslaus
wenzeslaus commented Apr 13, 2022

Is your feature request related to a problem?

Tests running on Ubuntu download the test data every time they run.

The URL is:

https://grass.osgeo.org/sampledata/north_carolina/nc_spm_full_v2alpha2.tar.gz

Describe the solution you'd like

GitHub Actions cache is used instead of a download inside the test script.

Describe alternatives you've considered

All these would

enhancement good first issue CI
timemachines

Illegal insider trading of stocks is based on releasing non-public information (e.g., new product launch, quarterly financial report, acquisition or merger plan) before the information is made public. Detecting illegal insider trading is difficult due to the complex, nonlinear, and non-stationary nature of the stock market. In this work, we present an approach that detects and predicts illegal insider trading proactively from large heterogeneous sources of structured and unstructured data using a deep-learning based approach combined with discrete signal processing on the time series data. In addition, we use a tree-based approach that visualizes events and actions to aid analysts in their understanding of large amounts of unstructured data. Using existing data, we have discovered that our approach has a good success rate in detecting illegal insider trading patterns. My research paper (IEEE Big Data 2018) on this can be found here: https://arxiv.org/pdf/1807.00939.pdf

  • Updated Jan 8, 2019
  • Python

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