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"abstract": "Folks who attend this session will gain a basic understanding of\ndifferent time series modeling and forecasting methods, both statistical\nand machine learning based.\n\nThis tutorial will include: - Why it is important to consider\ntime-series data differently than other data for modeling and\nforecasting. - How to process time-series data for modeling and\nforecasting purposes. - How to perform exploratory analysis and create\ninformative statistical plots to better understand time-series data\n(e.g., auto-correlation and partial auto- correlation, covariance\nmatrices). - Provide a couple of examples of statistical and machine\nlearning models to perform forecasting tasks. - Detail the strengths and\nweaknesses of different modeling methods through examples. - How to\nevaluate, interpret, and convey the output from forecasting models.\n",
"copyright_text": null,
"description": "Forecasting time-series data has applications in many fields, including\nfinance, health, etc. There are potential pitfalls when applying classic\nstatistical and machine learning methods to time-series problems. This\ntalk will give folks the basic toolbox to analyze time-series data and\nperform forecasting using statistical and machine learning models, as\nwell as interpret and convey the outputs.\n",