I would like to use Azure AutoML for forecasting where I have multiple features for one timeseries. Is there any example which I can replicate?
I have been looking into: https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb
and
https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
but no luck using multiple features instead of only one timeseries.
Any help is greatly appreciated
It looks like you are trying to find a notebook that shows how to predict a target variable when exogenous features are provided. The OJ sample notebook you included is actually a good example to reference for this scenario.
On a second glance, you'll find that in the OJ sample, `Quantity' is a function of 'Price' and other variables. We suggest trying to focus on a single time series within the OJ dataset (a single store & brand combo) as the concept could be lost in the focus on multiple series. Also note that in this example, the OJ dataset does have multiple features, we just only specify which features need to be excluded.
OJ Sample Notebook: https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
-Sabina, Azure Machine Learning PM
Please check here,
Auto-train a time-series forecast model - Azure Machine Learning | Microsoft Docs
Please check the below many models accelerator which models timeseries data (but in a different domain). This can be useful.
buswrecker/energy-many-models: An offshoot of the original AML Many-Models - for the Energy Sector (github.com)
AML AutoML forecasting models address missing data in featurization stage via forward fill if missing value is in target column or median value if in feature column. Also libraries like Prophet which are supported via Auto ML can be robust.
I am going thru the samples for Azure Machine Learning. It looks like the examples are leading me to the point that ML is being used to classification problems like ranking, classifying or detecting the category by model trained from inferred-sample-data.
Now that I am wondering if ML can be trained to computational problems like Multiplication, Division, other series problems,..? Does this problem fit in ML scope?
MULTIPLICATION DATASET:
Num01,Num02,Result
1,1,1
1,2,2
1,3,3
1,4,4
1,5,5
1,6,6
1,7,7
1,8,8
1,9,9
1,10,10
1,11,11
1,12,12
1,13,13
1,14,14
2,1,2
2,2,4
2,3,6
2,4,8
2,5,10
2,6,12
2,7,14
2,8,16
2,9,18
2,10,20
2,11,22
2,12,24
2,13,26
2,14,28
3,1,3
3,2,6
SCORING DATASET:
Num01,Num02
1,5
3,1
2,16
3,15
1,32
It seems like you are looking for regression, which is supportd by almost every machine learning library, including Azure's services. In laymans terms, the goal of regression is to approximate an unknown function that maps data X to a continuous value y.
This can be any function, indeed including multiplication or division. However, do note that these cases are usually way too simple to solve with machine learning. Most machine learning algorithms (except maybe linear regression)do a lot more internal computations and will as a result be slower than a native implementation on your device.
As an extra point of clarification, most of the actual machine learning (ML) in Azure ML is done by great open source libraries such as sk-learn or keras. Azure mainly provides compute power and higher-level management tools, such as experiment tracking and efficient hyper-parameter-tuning.
If you are just getting started with ML and want to go more in-depth, then this extra functionality might be overkill/confusing. So I would advise to start with focusing on one of the packages that I described above. Additionally you would need to combine that with some more formal training, which will explain most of the important concepts to you.
I want to create a web application which uses machine learning to predict the price of agriculture commodities before 2-3 months.
Is it really feasible or not?
If yes, then please provide some rough idea about which tools and technologies I can use to implement it.
First of all, study math, more precisely, statistics and differential algebra.
Then, use any open (or not) source neural networking libraries you could find. Even MATLAB would help, as it has a good set of examples (I think it has some of alike prediction models, at least I remember creating a model for predicting election results in Poland)
Decide on your training and input data. Research how news and global situation influences commodity prices. Research how existing bots predict prices for next 1-2 minutes. Also consider using history of predictions from certain individuals, I think Reuters has some API for this. Saying this I imply you'll have to integrate natural language processors, too.
Train your model, test it, improve it for quite a long time.
Finally, deploy a boring front-end and monetize it.
If you dont want to implement ML, you can also use kalman filters.
I want to predict my input price based on a list of questions/answers using azure machine learning.
I built one using the "bayesian linear regression" but it seems that it is predicting the price based on the prices i have in my dataset and not based on the Q/A.
Am i in the wrong path or am i missing something?
Any suggestion would be helpful.
Check the Q/A s that you using is not having missing values. If there's any missing values follow data preprocessing techniques to fill those.
What kind of answers do you have as inputs? (yes/no, numeric values, different textual answers, etc...) In my opinion numerical values and yes/no inputs makes your model more accurate.
Try different regression algorithms (https://azure.microsoft.com/en-us/documentation/articles/machine-learning-algorithm-cheat-sheet/) and check their accuracy.
you need to set features and label properly. if you publish your experiment in Gallery using unlisted mode and paste the link here, we can take a look.
I have developed a new image segmentation technique, and now I want to evaluate the performance. I am wondering how many sample do I need to perform the evaluation? In other words, how large is the testing sample set is sufficient in order to evaluate the new method? Any theory backup for that? Thanks.
There are standard computer vision datasets to benchmark segmentation. Example: http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/
You would have to report how your algorithm performs on these. Considering that the number of all possible images in the world is pretty big, these would constitute a good sample. ;-)