jeffrey-yau-applied-time-series-econometrics-in-python-and-r.json

{
  "copyright_text": "Standard YouTube License",
  "description": "PyData SF 2016\n\nTime series data is ubitious, and time series statistical models should be included in any data scientists\u2019 toolkit. This tutorial covers the mathematical formulation, statistical foundation, and practical considerations of one of the most important classes of time series models: the AutoRegression Integrated Moving Average with Explanatory Variables model and its seasonal counterpart.\n\nTime series data is ubitious, both within and out of the field of data science: weekly initial unemployment claim, tick level stock prices, weekly company sales, daily number of steps taken recorded by a wearable, just to name a few. Some of the most important and commonly used data science techniques to analyze time series data are those in developed in the field of statistics. For this reason, time series statistical models should be included in any data scientists\u2019 toolkit.\n\nThis 120-minute tutorial covers the mathematical formulation, statistical foundation, and practical considerations of one of the most important classes of time series models, AutoRegression Integrated Moving Average with Explanatory Variables (ARIMAX) models, and its Seasonal counterpart (SARIMAX).",
  "duration": 5981,
  "language": "eng",
  "recorded": "2016-08-24",
  "related_urls": [],
  "speakers": [
    "Jeffrey Yau"
  ],
  "tags": [
    "tutorial"
  ],
  "thumbnail_url": "https://i.ytimg.com/vi/tJ-O3hk1vRw/maxresdefault.jpg",
  "title": "Applied Time Series Econometrics in Python and R",
  "videos": [
    {
      "type": "youtube",
      "url": "https://www.youtube.com/watch?v=tJ-O3hk1vRw"
    }
  ]
}