Multiple Time Series Modeling using Facebook Prophet

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#timeseries #datascience #facebookprophet

Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well

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How can you achieve this if you have more variables that you want to add as regressor?

Icalldibsinthis

@AIEngineering, which one is better for multiple time series FBprophet Or DeepAR, and which one gives result fast??

VLM

great video. but these are modelling individual ts considering they are independently. how would u handle hiearchial ts, where each ts cud have dependency on others

koolkid

sir there we are concatenating multiple time series nn that means at same time we will have 2 values that means time stamp will not be unique nn.

manasapachavanitap

Which one is the multiple time series in distributed framework video?

sayantimondal

I did the similar use case for Retail domain. I liked the explanation and the coding style. I ended up following the long path. (This video is very helpful ).Are you planning to cover the hyper parameters for this library? For example - optimal Fourier order needs to be set? etc.

deeptigoyal

This is an awesome tutorial. Thank you so much for posting. I'm curious, are there ways of building useful forecasts if one or multiple time series have severely limited data? For example, what if one of the weather stations in your example were brand new and only had one or two days of data. Could we use what we know from the other time series in dataset to help forecast for the new weather station?

brianjemison

Multiprocessing will increase the speed of computation. But how would you tackle the problem of accuracy tuning as it cant be done for every single store even if they have slightly different seasonality?

aadilzikre

Sir, can we solve the same problem using LSTM algorithm or not ??

mohameddaly

Are there a tutorial for multiple time series forecasting with prophet ?. Thanks in advance

hadyshaaban

Is there a way to have a single model using Prophet (or other time series python lib) for all three store (for this we may need to provide station level info as onehot encoded vector)? This will then be more maintainable

shivkrishnajaiswal

Hello,

I was wondering if you could create a multiple time series forecasting model either Stat or ML?

richujose

Thank you for the video. Is the google colab notebook used in this video available?

gregoryweber

Hello sir...can you provide the video of the webinar you took on deployment of ML models in analytics vidya few days ago?

meghavenkatesh

Can we get this notebook you are using

rajatkumar

Hello Sir,
Firstly, Thanks a lot for the excellent content.
I want to know which model should be used if I have multiple time series for each group/product and having multivariate time series s.t it generated a global model not a model for each group ? I am looking for Unsupervised algorithm.

upasanawadhwa

Just found your channel, thank you sir for providing such excellent content! If I want to go through most / all of your courses, where should I start? I noticed some videos appear in more than one playlist, so I'm wondering if you have a recommended course / playlist sequence, thanks.

datastudent

Hello Sir,

You have mentioned in the end of the tutorial that in the next video you will teach how to run timeseries model in a more mutlithreaded way....but the next video's title is anomaly detection....can you please guide if I am missing anything ?

sankarshkadambari

Hello Sir,
Firstly, Thanks a lot for the excellent content.
Can LSTM used for Multiple Time Series Modeling?? And have you plan making a video about multiple time series modeling using LSTM??

parrot-media

@AIEngineering, How to handle multiple multivariate time series, seems quite complex, if possible please make a video on it...

VLM

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