case-study-in-travel-business-time-series-analysis-with-seasonal-data-cheuk-ting-ho.json

{
  "copyright_text": null,
  "description": "For time series analysis, everyone's talking about ARIMA or Holt-Winters. But there's other models which could also break down a seasonal series into trend, seasonality and noise. We will use an open source Python library called Seasonal to analyse B2B worldwide travel data.",
  "duration": 1515,
  "language": "eng",
  "recorded": "2018-05-26",
  "related_urls": [
    {
      "label": "Conference schedule",
      "url": "https://pydata.org/amsterdam2018/schedule/"
    },
    {
      "label": "Talk slides",
      "url": "https://www.slideshare.net/CheukTingHo/pydata-amsterdam-2018-time-series-analysis-with-seasonal-data-99093353"
    }
  ],
  "speakers": [
    "Cheuk Ting Ho"
  ],
  "tags": [],
  "thumbnail_url": "https://i.ytimg.com/vi/ZR9aAZZEhQo/maxresdefault.jpg",
  "title": "Case Study in Travel Business - Time Series Analysis with Seasonal Data",
  "videos": [
    {
      "type": "youtube",
      "url": "https://www.youtube.com/watch?v=ZR9aAZZEhQo"
    }
  ]
}