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<title>Week 10: Machine Learning Based time-series Analysis</title>
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<h2>Week 10: Machine Learning Based time-series Analysis</h2>
<h3>Materials:</h3>
<ul>
<li><a href="https://www.kaggle.com/learn/time-series" target="_blank">Review time-series</a></li>
<li>Applications: Forecasting--investment returns, sales, price, GDP, infulation etc</li>
<li>Machine Learning Models: XGBoost, LSTM, and GPTtime. <a href="https://medium.com/deep-data-science/advances-in-deep-learning-for-time-series-forecasting-classification-winter-2024-a3fd31b875b0">January 2024 update.</a></li>
<li><a href="https://unit8co.github.io/darts/">Darts is a Python library for user-friendly forecasting and anomaly detection on time series. It contains a variety of models, from classics such as ARIMA to deep neural networks.</a></li>
</ul>
<h3>Application and Exercises:</h3>
<ol>
<li>Perform time-series analysis on Store Sales. Apply machine learning models and explain the results.
<br><a href="https://www.kaggle.com/competitions/store-sales-time-series-forecasting/data">Data Source.</a></li>
<a href="https://www.kaggle.com/code/ekrembayar/store-sales-ts-forecasting-a-comprehensive-guide">Data Guide.</a>
<br><a href="https://www.kaggle.com/code/ferdinandberr/darts-forecasting-deep-learning-global-models/notebook">Sample Code.</a>
<li>Optional: Note that larges scale of time-series analysis is time consuming and computationally expensive. With managable dataset, perform time-series analysis on Stock returns. Apply machine learning models and explain the results.</li>
</ol>
<h3>Future reading and research:</h3>
<ol>
<li><a href="https://towardsdatascience.com/deep-gpvar-upgrading-deepar-for-multi-dimensional-forecasting-e39204d90af3">Deep GPVar Tutorial</a></li>
<li>Amazon Time Series Forecasting. <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html">Developer Guide.</a> </li>
</ol>
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