Time Series Analysis - 2 | Time Series in R | ARIMA Model Forecasting | Data Science | Simplilearn
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- Опубліковано 5 сер 2024
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This Time Series Analysis - 2 in R tutorial will help you understand what is ARIMA model forecasting, what is correlation, and auto-correlation. You will also see a use case implementation in which we forecast sales of air tickets using ARIMA. Finally, we will also look at how to validate a model using Ljung-Box text. Let's begin this time series in R video!
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The topics covered in this video on time series in R are:
Introduction 00:00:00
What Is Time Series 00:00:36
Introduction to ARIMA Model 00:03:04
Auto Correlation and Partial Auto Correlation 00:08:20
Use Case on ARIMA 00:10:03
Model validating using Ljung-Box text 00:15:08
Link to Time Series Analysis Part-1: • Time Series Analysis -...
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What Is R Programming?
R is an open-source programming language used for statistical computing. It is one of the most popular programming languages today. R was inspired by S+, which is similar to the S programming language. R has various data structures and operators. It can be integrated with other programming languages like C, C++, Java, and Python.
Time series is any data set where the values are measured at different points in time. Many times series are uniformly spaced at a specific frequency. Time series can also be irregularly spaced and sporadic. A time series is a sequence of data being recorded at specific time intervals. The past values are analyzed to forecast a future that is time-dependent. Compared to other forecast algorithms, with time series we deal with a single variable that is dependent on time.
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Do you have any questions on this topic? Please share your feedback in the comment section below and we'll have our experts answer it for you.
Thanks for watching the video. Cheers!
Was't the p value suppose to be below .05 while validating? Kindly help me understand.
A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis
A large p-value (> 0.05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis
p-values very close to the cutoff (0.05) are considered to be marginal (could go either way)
Hi Matt, thanks for watching our video. We regret to say that we don't have that particular video that you are requesting for. However, we will take your request into consideration and ask our instructors for this in future. Thanks.
Hi, the p values are greater than our level of significance (0.05) since we are using 95% CL..So is the model fit or not fit? I'm a little confused by your conclusion
I have just read some responses on the same above..Our null hypothesis states that the model is insignificant. So failing to reject makes us conclude that the model is not fit.Right?
Thank you, amazing video and helped me greatly to clear concept of Time Series
Hey, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
I have a timeseries data of production which is also dependent on machine dwntime and has seasonality component attached .. can u please suggest how do i consider this in my model .. forecasting accuracy only on time is very bad with auto ARIMA
Many thanks for this informative tutorial. I just want to ask why do apply lm regression as we going predict using forecasting function to do so ?
Great video! Very helpful. Thank you!
Hey, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)
It's very nice and detailed explanation , I have really enjoy it Thanks
Hey Meryem, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
quiet informative video. Thanks for posting it.
Hey Rituka, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
Amazing tutorial. Thank you very much!!!
You're very welcome!
mymodel
Wonderful explanation
Hey Baskar, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
Thanks alot. This video is really informative
Hello, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)
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 ?
Let me know if you figure the answer to your question.
15:28 How are the p values insignificant? I see that they are way above 0.05
thank your, sreeni sir
Very informative. Thanks
Glad it was helpful!
Hi! Is it possible two use ARIMA model for multi variable (i.e I have one intercept and 4 predictors to include in the model). For example : I want to predict Price using 3 independent parameters. Please, let me know how I can manage . Thanks
What is the meaning if our model example : ARIMA (0,1,1) with drift ? What is with drift ? And what funvtion to be used ?
Its really helpful video.
Hi Monu, we are glad you found our video helpful. Do subscribe and stay tuned for updates on our channel. Cheers :)
Thanks a lott SIR !
You are very welcome!
Thank you
It is our pleasure! Check out ua-cam.com/video/ukzFI9rgwfU/v-deo.html for more videos! Happy learning!
I could not run auto.arima function. Do I need any special package for this?
very well explained!
Glad it was helpful!
What does smoothening done by us in previous video achieve? Can you elaborate?
thank you it is very informative , can you plz make one video on manual fitting of time series and fitting using minitab
Hi Swati, we are glad you found our video informative. We will definitely look into your suggestions. Do subscribe and stay tuned for updates on our channel. Cheers :)
How may I check whether the forecasted data has an error or not. If yes, how to calculate that error?
thak u sir
You are welcome!
Sir, thanks for valuable lecture. Sir, I have a question ie. ---- should I go for data partitioning and data decomposition before fitting into auto.arima ?? Hope you will be kind enough to guide me .With thanks.
Yes, you can
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
Hello, thank you for watching our video. We will definitely look into your suggestions. Do subscribe and stay tuned for updates on our channel. Cheers :)
Do I need any package to plot the original data i.e plot(AirPassengers) It is working on my R
You can use R plot library to plot the data points.
Can u say how to calculate st (H column ) in excel sheet
Thank you! Amazing video. But I have a question about the ARIMA model you present. In the "my model" it said ARIMA(2,1,1)(0,1,0), but after adding "ic=aic, trace=TRUE" of auto.arima it gets ARIMA(2,1,1)(2,1,0)?
Go with Auto.arima trace option as it will allow you to check first hand that aic value are lowest in whole range.
if i have a data set that is not stationary. How do i get it to be stationary? Is it solely by doing log(data) since auto.arima will automatically get my p,d,q coefficients? If that is the case how do i get my predicted values back in the original figures and not the logarithmic values?
Hi Yoruba, thanks for checking out our tutorial. For a non-stationary time-series, you can also differentiate your data as many times as required to achieve stationarity (recommended: twice). And if the time series has a seasonal component, you differentiate at the seasonal period as well. Alternatively, you could fit a smoothing spline and subtract it from your original data (see: smooth.spline in R)
while testing the model, when I reduced the lag to 3 or 2 the accuracy of model increased 89% .
Could you please explain why is this happening?
what should be the best value of and how to select the same?
Hi Chaitali, thanks for watching our video. The lag should be constant throughout, you can choose whichever increases the accuracy.
for the last part model test using the Box.test on the model for different lag values, you mentioned the p-values are reasonably low. What is the cut-off value? I have thought it is 0.05, but those p-values are greater than 0.05.
Hi, thanks for checking out our tutorial. The p-value is the most important concept for the analysis of results of a (trend) test. It represents the probability of an error when considering the real value of the estimated parameter differs from the computed (or static) one, e.g., that the zero hypothesis holds although we considered the alternative one. Usually, if the p-value is under 5%, we accept the alternative hypothesis, because the risk of its invalidity is relatively low.
in box test P value is coming more than. 05 so our model is not good. it's insignificant.
Thanks for your input!
sir my project is crime forecasting
i use code in R studio crime_arima
Crime forecasting..that's a really thing you picked up. What made you choose that?
Is it suitable to use linear regression instead of arima when working with time series?
Linear regression will only capture the trend of a time series and will not catch seasonality. So, if you are forecasting yearly numbers, linear regression is often sufficient. If you need to forecast on a monthly/weekly/daily level, you'll need to do something like ARIMA. But remember, ARIMA is a type of regression. It is just the integration of many regressions.
Thanks for your valuable input!
Hi . Could you share the Air-passengers dataset used for this video ?
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Sir in last step Box.test , p-values are not reasonably low rather they are higher than 0.05, so we accept Null-Hypothesis.
Hi, Simplilearn provides online training across the world. We would be happy to help you regarding this. Please visit us at www.simplilearn.com and drop us a query and we will get back to you! Thanks!
how can we know the coefficient values of fitted model...
Hi there, In time series analysis, the coefficient values of a fitted model can be obtained through various methods depending on the specific modeling approach used. Commonly used techniques are Ordinary Least Squares (OLS), Maximum Likelihood Estimation, and Non-linear Optimization.
my data is in a dataframe, how do i change this to a time series, so i can follow online with your examples?
Ex:
Serie
Thanks for your valuable input!
Could you please share the excel used in the previous video ?
Hi, thanks for checking our video. Please provide us with your email ID and we will share the requested dataset immediately. If you want our email ID to be kept hidden, we can do that as well. Thanks.
Hello Vijay, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
i don't care if still don't know how to get ST in Moving Average Mode, i'll watch it
Thanks for watching our video. Do subscribe to our channel and stay tuned.
i need help
Hi, we are glad to help you. Please do elaborate on your requirement/doubt. Thanks.
@@SimplilearnOfficial your whatsapp r fb?
You can check our FB page by clicking here: facebook.com/Simplilearn/. Cheers!
The way it was explained in this video while R coding on the use case is not that much intuitive.
Please explain clearly.
Thank you for your review. I’m sorry to hear you had such a frustrating experience, but I really appreciate you bringing this issue to my attention
how the model is calculated?