.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_poisson_hmm.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_plot_poisson_hmm.py: Using a Hidden Markov Model with Poisson Emissions to Understand Earthquakes ---------------------------------------------------------------------------- Let's look at data of magnitude 7+ earthquakes between 1900-2006 in the world collected by the US Geological Survey as described in this textbook: Zucchini & MacDonald, "Hidden Markov Models for Time Series" (https://ayorho.files.wordpress.com/2011/05/chapter1.pdf). The goal is to see if we can separate out different tectonic processes that cause earthquakes based on their frequency of occurance. The idea is that each tectonic boundary may cause earthquakes with a particular distribution of waiting times depending on how active it is. This might tell help us predict future earthquake danger, espeically on a geological time scale. .. GENERATED FROM PYTHON SOURCE LINES 15-42 .. code-block:: default import numpy as np import matplotlib.pyplot as plt from scipy.stats import poisson from hmmlearn import hmm # earthquake data from http://earthquake.usgs.gov/ earthquakes = np.array([ 13, 14, 8, 10, 16, 26, 32, 27, 18, 32, 36, 24, 22, 23, 22, 18, 25, 21, 21, 14, 8, 11, 14, 23, 18, 17, 19, 20, 22, 19, 13, 26, 13, 14, 22, 24, 21, 22, 26, 21, 23, 24, 27, 41, 31, 27, 35, 26, 28, 36, 39, 21, 17, 22, 17, 19, 15, 34, 10, 15, 22, 18, 15, 20, 15, 22, 19, 16, 30, 27, 29, 23, 20, 16, 21, 21, 25, 16, 18, 15, 18, 14, 10, 15, 8, 15, 6, 11, 8, 7, 18, 16, 13, 12, 13, 20, 15, 16, 12, 18, 15, 16, 13, 15, 16, 11, 11]) # Plot the sampled data fig, ax = plt.subplots() ax.plot(earthquakes, ".-", ms=6, mfc="orange", alpha=0.7) ax.set_xticks(range(0, earthquakes.size, 10)) ax.set_xticklabels(range(1906, 2007, 10)) ax.set_xlabel('Year') ax.set_ylabel('Count') fig.show() .. image-sg:: /auto_examples/images/sphx_glr_plot_poisson_hmm_001.png :alt: plot poisson hmm :srcset: /auto_examples/images/sphx_glr_plot_poisson_hmm_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 43-44 Now, fit a Poisson Hidden Markov Model to the data. .. GENERATED FROM PYTHON SOURCE LINES 44-67 .. code-block:: default scores = list() models = list() for n_components in range(1, 5): for idx in range(10): # ten different random starting states # define our hidden Markov model model = hmm.PoissonHMM(n_components=n_components, random_state=idx, n_iter=10) model.fit(earthquakes[:, None]) models.append(model) scores.append(model.score(earthquakes[:, None])) print(f'Converged: {model.monitor_.converged}\t\t' f'Score: {scores[-1]}') # get the best model model = models[np.argmax(scores)] print(f'The best model had a score of {max(scores)} and ' f'{model.n_components} components') # use the Viterbi algorithm to predict the most likely sequence of states # given the model states = model.predict(earthquakes[:, None]) .. rst-class:: sphx-glr-script-out .. code-block:: none Converged: True Score: -391.9189281654951 Converged: True Score: -391.9189281654951 Converged: True Score: -391.9189281654951 Converged: True Score: -391.9189281654951 Converged: True Score: -391.9189281654951 Converged: True Score: -391.9189281654951 Converged: True Score: -391.9189281654951 Converged: True Score: -391.9189281654951 Converged: True Score: -391.9189281654951 Converged: True Score: -391.9189281654951 Converged: True Score: -341.89397049875356 Converged: True Score: -341.88244772721345 Converged: True Score: -342.1445482378532 Converged: True Score: -341.8929674859709 Converged: True Score: -341.8855538199329 Converged: True Score: -342.2876227612777 Converged: True Score: -342.5369292103593 Converged: True Score: -341.8875020776218 Converged: True Score: -341.8789363379975 Converged: True Score: -342.97038817436965 Converged: True Score: -343.0429169039883 Converged: True Score: -342.08452039551764 Converged: True Score: -342.6892743201981 Converged: True Score: -328.5977427805311 Converged: True Score: -341.66130011064183 Converged: True Score: -329.06399427716025 Converged: True Score: -329.50502535576027 Converged: True Score: -338.9078699324075 Converged: True Score: -328.7562782189654 Converged: True Score: -337.6515744961049 Converged: True Score: -328.14045680319686 Converged: True Score: -340.59777383065585 Converged: True Score: -330.46705382979616 Converged: True Score: -341.56334292712455 Converged: True Score: -328.0212654423776 Converged: True Score: -340.86474747756466 Converged: True Score: -339.5597855572184 Converged: True Score: -328.6984713056866 Converged: True Score: -328.8760462922794 Converged: True Score: -340.13188775835465 The best model had a score of -328.0212654423776 and 4 components .. GENERATED FROM PYTHON SOURCE LINES 68-72 Let's plot the waiting times from our most likely series of states of earthquake activity with the earthquake data. As we can see, the model with the maximum likelihood had different states which may reflect times of varying earthquake danger. .. GENERATED FROM PYTHON SOURCE LINES 72-80 .. code-block:: default # plot model states over time fig, ax = plt.subplots() ax.plot(model.lambdas_[states], ".-", ms=6, mfc="orange") ax.plot(earthquakes) ax.set_title('States compared to generated') ax.set_xlabel('State') .. image-sg:: /auto_examples/images/sphx_glr_plot_poisson_hmm_002.png :alt: States compared to generated :srcset: /auto_examples/images/sphx_glr_plot_poisson_hmm_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Text(0.5, 23.52222222222222, 'State') .. GENERATED FROM PYTHON SOURCE LINES 81-85 Fortunately, 2006 ended with a period of relative tectonic stability, and, if we look at our transition matrix, we can see that the off-diagonal terms are small, meaning that the state transitions are rare and it's unlikely that there will be high earthquake danger in the near future. .. GENERATED FROM PYTHON SOURCE LINES 85-92 .. code-block:: default fig, ax = plt.subplots() ax.imshow(model.transmat_, aspect='auto', cmap='spring') ax.set_title('Transition Matrix') ax.set_xlabel('State To') ax.set_ylabel('State From') .. image-sg:: /auto_examples/images/sphx_glr_plot_poisson_hmm_003.png :alt: Transition Matrix :srcset: /auto_examples/images/sphx_glr_plot_poisson_hmm_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Text(31.222222222222214, 0.5, 'State From') .. GENERATED FROM PYTHON SOURCE LINES 93-96 Finally, let's look at the distribution of earthquakes compared to our waiting time parameter values. We can see that our model fits the distribution fairly well, replicating results from the reference. .. GENERATED FROM PYTHON SOURCE LINES 96-113 .. code-block:: default # get probabilities for each state given the data, take the average # to find the proportion of time in that state prop_per_state = model.predict_proba(earthquakes[:, None]).mean(axis=0) # earthquake counts to plot bins = sorted(np.unique(earthquakes)) fig, ax = plt.subplots() ax.hist(earthquakes, bins=bins, density=True) ax.plot(bins, np.dot(poisson.pmf(bins, model.lambdas_).T, prop_per_state[:, None])) ax.set_title('Histogram of Earthquakes with Fitted Poisson States') ax.set_xlabel('Number of Earthquakes') ax.set_ylabel('Proportion') plt.show() .. image-sg:: /auto_examples/images/sphx_glr_plot_poisson_hmm_004.png :alt: Histogram of Earthquakes with Fitted Poisson States :srcset: /auto_examples/images/sphx_glr_plot_poisson_hmm_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 1.179 seconds) .. _sphx_glr_download_auto_examples_plot_poisson_hmm.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_poisson_hmm.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_poisson_hmm.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_