In this session, we will see how to develop your Time series based forecasting app.
OK, that is a set of values that is dependent on time, is fed into the app for processing, and we
will have the future set of values.
Example can be a member of sales in a month done by a company that is dependent on time.
Right.
That is in the month of January.
A certain number of sales happen in the month of February.
A certain number of sales happen, so on and so forth.
Let's say you have five years of data and you want to know what will be the future data points.
That is.
Let's see if you have data from twenty sixteen to twenty twenty one and you want to know what will be
the sales values for each of the month in 2022.
This app can be used OK.
Which is what I'm going to explain in this particular session.
OK?
Before seeing how to build the forecasting app, let's understand the forecasting process in the table
that you see on your screen.
We have five data points corresponding to five months.
So this is Time-Based data and we can use time series forecasting to determine the values for the month
of June and beyond.
Obviously, the number of data points is fewer here.
Algorithmic approach is helpful and data is complex and huge.
As part of Time series forecasting, we uncover three things seasonality, trend and residual.
Certainly something that gets repeated over and over again.
Festivals come during certain months, every year, and sales could increase around the time, and this
happens every year.
So this is a repetitive phenomenon.
When seasonality is long term, trend is shorter, but both are cyclical events, these needs to be
factored in our forecasting process.
When you remove seasonality and trend from the data points, we get residuals.
Forecasting is actually done for deals, and we incorporate seasonality and trend into the forecasted
values to arrive at the final set of forecasted numbers for the purposes of forecasting the residues
we use Arima Auto regressive, integrated moving average, the process of splitting the data into seasonality,
trend and residual and subsequently forecasting of residuals back backflipping seasonality and trend
into the forecasted values.
All of this is done by an algorithm of many algorithms that are in the market today.
I find Facebook's profit algorithm to be much superior four time series forecasting due to very high
forecast accuracy.
So we will use this profit algorithm in building the forecasting app.
So I'm asking the eBay app.
I'm going to explain the course using flowchart because it is helpful to understand what is going on.
OK, flowchart is easier to understand and process.
OK, so now let's get started.
What we do is we have to first import the necessary libraries.
That is the first step that we do whenever we are developing.
Any particular rap, and that's what we do in machine learning programming, also, we import the necessary
libraries and then us go into the court system.
OK, so we have imposed extremely bonders.
Lochley, that is for displaying the graph.
And then if we profit, profit is a wonderful library.
In fact, it is from Facebook.
And in fact it's one of the best libraries when it comes to TIME series based forecasting.
In fact, we use that very extensively with our clients and I regularly see very high forecasting accuracy
with Facebook profit.
If you have Time series based forecasting needs, I strongly encourage you to use profit.
OK, so now let's get into the courts.
You start with Tytler model that you we want to mention.
What does does.
We also provide the contact details and we provide a list of libraries right now.
You can mention any of the details also.
Right.
And then we applaud the fine.
Applauding the fine fine underscored by that is the variable that I declare and then I use fine underscore
uploader to upload the file, I clearly mentioned the type of file that is going to be uploaded.
Right.
The next step is I have uploaded the file.
I want to know whether the file has been uploaded or not.
If it is uploaded.
I want the file to be read for for the processing of the file underscored by the variable that we declare
is not.
No.
That means the file has been uploaded.
We want the values to be displayed.
OK, now the file is uploaded, the processing has to start, but before that, I want to know a number
of days that I want the forecasting to be done.
Do I want, for all data points, 15 data points under data points, depending on your need.
So you mentioned the number of days that you want to forecast up and it can be a number of months.
Number of data points.
It is for you to declare.
Right.
So here we provide a sidebar of a selection box.
Right.
Astrocytoma Dot Seligman's.
You mentioned the number of days and I also provide a range.
You can display the range also.
It's entirely up to you.
OK, and I mentioned the select the number of days we want to forecast.
OK, and then we forecast U.S. profit, basically fitting the model.
As I said, it's a library.
So it comes pre trained.
You don't need to bring the model of the library here.
It's ready to use just three or four lines of code.
The forecast is complete for us and then we are going to plot the forecast.
Claire.
So this is the set of codes for developing the forecasting app.
Now let's run the file and I will show you how the app looks, how the forecasting is done.
OK.
What do you see here is the code for forecasting at this time showing to you in notepad?
The file has been saved.
Also time, Chris, have not revised, already saved.
Let's come to the Stingley folder to see if it is there.
Time series is also.
Appearing here on.
Now, let's run the code in Anaconda command prompt real fast, change the direction.
Nord, north Queensland city, not city, dodged a bullet.
Now Tremlett.
Ron.
Osas, what time, time cities have dropped by?
We'll run it.
The euro will appear shortly.
OK.
And the app appears in our default browser, so simple stream let up.
OK, time series forecast.
You can give any kind of.
OK, so I'm going to upload the five year.
This is the file that I'm uploading, OK?
It's been uploaded now.
You can see the name appearing.
It indicates that the file has been uploaded and I can choose the number of days you want to forecast.
Is it right what you see here?
This is 410.
And profit library also provides the upper end of the range and lower end of the range when it comes
to forecasting.
Normally when you forecast it is normally within a range, right.
You say that the value of beam is expected and is likely to be in the range between, say, 13 and 17.
Right.
Because forecasting is always in a range.
Right.
Because forecast accuracy can never be 100 percent clear.
OK, I can also choose some other member of base.
Also see here, one twenty seven, one twenty eight.
Any kind of value within the range that we have defined in our program can be chosen, right.
It's entirely up to us.
So I'm choosing 120 days.
And here are the set of values for 120 days.
Clear, we can even structure that up in such a way that the values can be downloaded.
OK, we can change the way the values are presented.
It's entirely up to us, those kind of modifications are very much possible.
Right.