The right time Assortment Forecasting with the use of Facebook Predictor and Python in 20 to 30 minutes

Time Series Forecasting with Facebook Prophet and Python in 20 Minutes

Trying to forecast the next best stock?

Want to predict the weather?

Maybe you’re just trying to get a better sales forecast for your small business!

Time series forecasting can help!

In this video you’ll learn how to QUICKLY use time series forecasting to produce a forecast. In just a couple of minutes you’ll be able to preprocess your dataset using Pandas and forecast over a number of time periods using Facebook Prophet.
In this video you’ll learn how to:
1. Preparing Data for Time Series FC
2. Training Prophet Time Series Models
3. Making forecast predictions

GET THE CODE!
https://github.com/nicknochnack/TimeSeriesForecastingProphet

Links Mentioned:
Facebook Prophet: https://facebook.github.io/prophet/docs/quick_start.html

If you have any questions, please drop a comment below!

Oh, and don’t forget to connect with me!
LinkedIn: https://www.linkedin.com/in/nicholasrenotte
Facebook: https://www.facebook.com/nickrenotte/
GitHub: https://github.com/nicknochnack

Happy coding!
Nick

P.s. Let me know how you go and drop a comment if you need a hand!

SLIDES: https://docs.google.com/presentation/d/1DbBAKCcbYOjdxUtUD6aLpB_rbg3ikHzdPSnUztw2GqA/edit#slide=id.g9f34cc2927_0_0
Video Rating: / 5

10 Antworten auf „The right time Assortment Forecasting with the use of Facebook Predictor and Python in 20 to 30 minutes“

  1. Hey good video and explanation man, appreciate it!

    Out of curiosity, do you know if there is a way to produce time series forecasting with multiple variables? For example, if "Product" and "Store" had multiple categories in it, what would we use?

  2. Great video! Just one question; how is hourly seasonality available when you have not specified any hours on the dataset? The data seems to be total sales/day for a single product in a single location.

  3. Why Dropped Store & Product ??????????????????????

    What is the VAlue of Forecast if it is not at STORE+PRODUCT LEVEL ???????

    How will we use this FORECAST ???????????????????????

    NOT USEFUL

  4. How was the model able to determine the daily seasonality when in fact you did not pass any intra-day (minute) data?!

    Really good video walkthrough;

    Keep up the good work!

  5. The explanation was very clear. I'm working on a dataset where i have many different cities solar data. I want to predict the irradiance value for each city rather than just one. I know you touched on this briefly in your video, is there any tutorial on this?

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