I'm trying to work out how to conduct true ARIMA out of sample forecasting in Python. I've been googling for DAYS and there doesn't seem to be an answer.
Say I have a dataset of 75 integers. I'm splitting this dataset 70/30 (train/test) as I would do with any ML model, I'm training the model on train, testing against test, but after training and testing I want to predict values 76-80 which do not currently exist. How is this done? Using the build in predict and forecast function beyond the initial dataset throws back errors because my existing dataset isn't long enough.
Do I need to add artificial data to the dataset to enable this? A point in the right direction would be fantastic.
Apologies for the formatting - submitted via phone.
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I have weekly data with 15 predictor variables for a period of 1 year (52 observations).
I plan to compare Prophet forecasting with VAR model.
Is there a way to run cross-validation for these 2 models especially the VAR.
Thanks
HP
A good explainer on time series cross validation from the forecasting principles and practice book here
Time series cross-validation is done by splitting training data up to some point in time (typically between 2/3 or 4/5) and using the remainder as validation. Then at each step fit a model to the training data, make an out-of-sample prediction, store that prediction, and add the next data point to your training data.
So at the least step, you're fitting your training model on all of your training data except for a single data point since you'll be comparing that single datapoint to what your model forecasts at this last step. You then can do root mean squared or whatever on the list of predictions you have versus their actual values. In this way, you are testing how appropriately your model can fit this dataset over time.
For Prophet, the docs list an easy way to do it in python.
For VAR, I don't know of an easy way, other than looping over the training data to make forecasts, appending the next timestamp at each step, and then comparing to the validation data.
I am looking to gain a better understanding of how my model learns a particular dataset. I wanted to visualize the training and eval phases of learning by plotting the actual training/eval data alongside model predictions for the same.
I got the idea from observing some Matlab code, which allows the user to plot the above mentioned values. Unfortunately I no longer have access to the Matlab code and would like to recreate the same in Python.
Using the code below:
model = xgb.train(params, dtrain,evals=watchlist,evals_result=results,verbose_eval=False)
I can get a results dictionary which saves, the training and eval rmse values as shown below:
{'eval': {'rmse': [0.557375, 0.504097, 0.449699, 0.404737, 0.364217, 0.327787, 0.295155, 0.266028, 0.235819, 0.212781]}, 'train': {'rmse': [0.405989, 0.370338, 0.337915, 0.308605, 0.281713, 0.257068, 0.234662, 0.214531, 0.195993, 0.179145]}}
While the output shows me the rmse values, I was wondering whether there is a way to get the predicted values for both the training as well as eval set, using which these rmse values are calculated.
I am using the Azure ML model available at https://gallery.azure.ai/Experiment/Weather-prediction-model-1 to design a prediction mechanism based on temperature and humidity. I haven't done any changes to the existing model and feeding in data from a simulator. The prediction output is stuck at 0.489944100379944. I have taken over 17k samples and still, the prediction is constant at this value.
Any help will be highly appreciated.
N.B. - This is my first stint with ML
This was caused by the training dataset. The dataset had characters in the humidity and temperature columns. This led to the model expecting characters but operating on floating point numbers. I cleaned the dataset and ensured that there are only floats in the temperature and humidity columns. Then I used this training data for the model and phew!!!! Everything's working now.
Is there a way to get the significance level of each coefficient we receive after we fit a logistic regression model on training data?
I was trying to find out a way and could not figure out myself.
I think I may get the significance level of each feature if I run chi sq test but first of all not sure if I can run the test on all features together and secondly I have numeric data value so if it will give me right result or not that remains a question as well.
Right now I am running the modeling part using statsmodel and scikit learn but certainly, want to know, how can I get these results from PySpark ML or MLLib itself
If anyone can shed some light, it will be helpful
I use only mllib, I think that when you train a model you can use toPMML method to export your model un PMML format (xml file), then you can parse the xml file to get features weights, here an example
https://spark.apache.org/docs/2.0.2/mllib-pmml-model-export.html
Hope that will help
So I have been playing around with Azure ML lately, and I got one dataset where I have multiple values I want to predict. All of them uses different algorithms and when I try to train multiple models within one experiment; it says the “train model can only predict one value”, and there are not enough input ports on the train-model to take in multiple values even if I was to use the same algorithm for each measure. I tried launching the column selector and making rules, but I get the same error as mentioned. How do I predict multiple values and later put the predicted columns together for the web service output so I don’t have to have multiple API’s?
What you would want to do is to train each model and save them as already trained models.
So create a new experiment, train your models and save them by right clicking on each model and they will show up in the left nav bar in the Studio. Now you are able to drag your models into the canvas and have them score predictions where you eventually make them end up in the same output as I have done in my example through the “Add columns” module. I made this example for Ronaldo (Real Madrid CF player) on how he will perform in match after training day. You can see my demo on http://ronaldoinform.azurewebsites.net
For more detailed explanation on how to save the models and train multiple values; you can check out Raymond Langaeian (MSFT) answer in the comment section on this link:
https://azure.microsoft.com/en-us/documentation/articles/machine-learning-convert-training-experiment-to-scoring-experiment/
You have to train models for each variable that you going to predict. Then add all those predicted columns together and get as a single output for the web service.
The algorithms available in ML are only capable of predicting a single variable at a time based on the inputs it's getting.