bootstrapped prediction intervals

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Time Series Analysis - 2 | Time Series in R | ARIMA Model Forecasting | Data Science | Simplilearn

This Time Series Analysis (Part-2) in R tutorial will help you understand what is ARIMA model, what is correlation & auto-correlation and you will alose see a use case implementation in which we forecast sales of air-tickets using ARIMA and at the end, we will also how to validate a model using Ljung-Box text.

Link to Time Series Analysis Part-1: https://www.youtube.com/watch?v=gj4L2isnOf8

You can also go through the slides here: https://goo.gl/9GGwHG

A time series is a sequence of data being recorded at specific time intervals. The past values are analyzed to forecast a future which is time-dependent. Compared to other forecast algorithms, with time series we deal with a single variable which is dependent on time. So, lets deep dive into this video and understand what is time series and how to implement time series using R.

Below topics are explained in this “ Time Series in R Tutorial “ –

1. Introduction to ARIMA model
2. Auto-correlation & partial auto-correlation
3. Use case – Forecast the sales of air-tickets using ARIMA
4. Model validating using Ljung-Box test

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23 Antworten auf „bootstrapped prediction intervals“

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  2. Hello, thank you for your efforts – this clarified a bunch of things for me.
    I tried your exact tutorial and my results varied when i ran the aic trace – it returned a different mode (01,1) . im wondering what could give us 2 different results ?

  3. it would be very convenient for users if you make a same video with illustration with python . you guys doing a great job ,
    or as a suggestion you can make that video like how to convert your code from R to python @simplilearn

  4. sir my project is crime forecasting
    i use code in R studio crime_arima <- auto.arima(crime_ts,ic="aic",trace=TRUE)
    then my best model is (0,0,0). so i confuse how to forecasting
    plz solve my querry

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