The usual way to fit an ARIMA model with the statsmodels python package is:
model = statsmodels.tsa.ARMA(series, order=(2,2))
result = model.fit(trend='nc', disp=1)
however, i have multiple time series data to train with, say, from the same underlying process, how could i do that?
When you say, multiple time series data, it is not clear if they are of the same type. There is no straightforward way to specify multiple series in ARMA model. However you could use the 'exog' optional variable to indicate the second series.
Please refer for the actual definition of ARMA model.
model = statsmodels.tsa.ARMA(endog = series1, exog=series2, order=(2,2))
Please refer for the explanation of the endog, exog variables.
Please see a working example of how this could be implemented
Related
I'm trying to pass sample weights to a scikit learn ensemble with the following structure, but I can't find a way to navigate the interaction between the VotingRegressor and the Pipeline.
ensemble = VotingRegressor(\[
('m1Pipeline',Pipeline(\[('getFeaturesModel1',feature_transformer_1),('m1',Model1())\])),
('m2Pipeline',Pipeline(\[('getFeaturesModel2',feature_transformer_2),('m2',Model2())\]))
\])
It's designed this way because I need to provide specific features to the first model, and specific features to the second model (which are different from the first model), and I need to average their outputs.
First, I tried passing an overall sample weight, since both underlying models support it, and therefore I expected the VotingRegressor to accept it at the top level:
ensemble.fit(X,Y,sample_weight=weights)
ValueError: Pipeline.fit does not accept the sample_weight parameter. You can pass parameters to specific steps of your pipeline using the stepname__parameter format, e.g. Pipeline.fit(X, y, logisticregression__sample_weight=sample_weight).
I then tried to pass the sample weights to the individual models:
ensemble.fit(X,Y,m1Pipeline__m1__sample_weight=weights,m2Pipeline__m2__sample_weight=weights)
fit_params={'vr__en__modelEN__sample_weight':weights,'vr__rf__modelRF__sample_weight':weights})
TypeError: fit() got an unexpected keyword argument 'model1Pipeline__model1__sample_weight'
By the way, I tried extending this design pattern, which does work:
ensemble = VotingRegressor(\[('model1',Model1())\])),('model2',Model2())\])
ensemble.fit(X,Y,sample_weight=weights)
Any suggestions on how I can accomplish this would be much appreciated!
My independent variable is a datetime object and my dependent variable is an float. Currently, I have a keras model that predicts accurately, but I found out that model.predict() only returns predictions for the values that are already known. Is there a method I can call to tell the program to use the model to predict unknown values? If there isn't please give me instructions about how to predict these unknown values.
Currently, I have a Keras model that predicts accurately, but I found out that model.predict() only returns predictions for the values that are already known
That is incorrect. A predict statement doesn't just 'search and return' results from training data. That's not how machine learning works at all. The whole reason that you build models and have a train and test dataset is to ensure you have a model that is generalizable (i.e. can be used to make predictions on unseen data, assuming the observation is coming from the same underlying distribution that the model is trained on)
In your specific case, you are using a DateTime variable an independent, which means you should refrain from using variable such as year, which are non-recurring since you can use it to make predictions about the future (model learns patterns in 2019 but 2020 may be out of its vocabulary and thus years after that are not feasible to use for predictions.)
Instead, you should engineer some features from your DateTime variable and use recurring variables which may show reveal some patterns in the dependent variable. These variables are like days of the week, months, seasons, hours of the day. Depending on what your dependent variable is, you can surely find some patterns in these.
All of this totally depends on what you are trying to model and what is the goal of the model.predict() w.r.t your problem statement. Please elaborate if possible so that people can give you more specific answers.
Your assumption is incorrect. model.predict is specifically intended to use a trained model to make predictions on a data set typically not used previously for example a test set and not a training or validation set. To use it you need to create a data set to feed to model.predict. See answer here. on how to provide input to model.predict
I have an already existing ARIMA (p,d,q) model fit to a time-series data (for ex, data[0:100]) using python. I would like to do forecasts (forecast[100:120]) with this model. However, given that I also have the future true data (eg: data[100:120]), how do I ensure that the multi-step forecast takes into account the future true data that I have instead of using the data it forecasted?
In essence, when forecasting I would like forecast[101] to be computed using data[100] instead of forecast[100].
I would like to avoid refitting the entire ARIMA model at every time step with the updated "history".
I fit the ARIMAX model as follows:
train, test = data[:100], data[100:]
ext_train, ext_test = external[:100], external[100:]
model = ARIMA(train, order=(p, d, q), exog=ext_train)
model_fit = model.fit(displ=False)
Now, the following code allows me to predict values for the entire dataset, including the test
forecast = model_fit.predict(end=len(data)-1, exog=external, dynamic=False)
However in this case after 100 steps, the ARIMAX predicted values quickly converge to the long-run mean (as expected, since after 100 time steps it is using the forecasted values only). I would like to know if there is a way to provide the "future" true values to give better online predictions. Something along the lines of:
forecast = model_fit.predict_fn(end = len(data)-1, exog=external, true=data, dynamic=False)
I know I can always keep refitting the ARIMAX model by doing
historical = train
historical_ext = ext_train
predictions = []
for t in range(len(test)):
model = ARIMA(historical, order=(p,d,q), exog=historical_ext)
model_fit = model.fit(disp=False)
output = model_fit.forecast(exog=ext_test[t])[0]
predictions.append(output)
observed = test[t]
historical.append(observed)
historical_ext.append(ext_test[t])
but this leads to me training the ARIMAX model again and again which doesn't make a lot of sense to me. It leads to using a lot of computational resources and is quite impractical. It further makes it difficult to evaluate the ARIMAX model cause the fitted params to keep on changing every iteration.
Is there something incorrect about my understanding/use of the ARIMAX model?
You are right, if you want to do online forecasting using new data you will need to estimate the parameters over and over again which is computationally inefficient.
One thing to note is that for the ARIMA model mainly the estimation of the parameters of the MA part of the model is computationally heavy, since these parameters are estimated using numerical optimization, not using ordinary least squares. Since after calculating the parameters once for the initial model you know what is expected for future models, since one observation won't change them much, you might be able to initialize the search for the parameters to improve computational efficiency.
Also, there may be a method to do the estimation more efficiently, since you have your old data and parameters for the model, the only thing you do is add one more datapoint. This means that you only need to calculate the theta and phi parameters for the combination of the new datapoint with all the others, while not computing the known combinations again, which would save quite some time. I very much like this book: Heij, Christiaan, et al. Econometric methods with applications in business and economics. Oxford University Press, 2004.
And this lecture might give you some idea of how this might be feasible: lecture on ARIMA parameter estimation
You would have to implement this yourself, I'm afraid. As far as I can tell, there is nothing readily available to do this.
Hope this gives you some new ideas!
As this very good blog suggests (3 facts about time series forecasting that surprise experienced machine learning practitioners):
"You need to retrain your model every time you want to generate a new prediction", it also gives the intuitive understanding of why this happens with examples. That basically highlights time-series forecasting challenge as a constant change, that needs refitting.
I was struggling with this problem. Luckily, I found a very useful discussion about it. As far as I know, the case is not supported by ARIMA in python, we need to use SARIMAX.
You can refer to the link of discussion: https://github.com/statsmodels/statsmodels/issues/2788
Is there a straightforward way to view the top features of each class? Based on tfidf?
I am using KNeighbors classifer, SVC-Linear, MultinomialNB.
Secondly, I have been searching for a way to view documents that have not been classified correctly? I can view the confusion matrix but I would like to see specific documents to see what features are causing the misclassification.
classifier = SVC(kernel='linear')
counts = tfidf_vectorizer.fit_transform(data['text'].values).toarray()
targets = data['class'].values
classifier.fit(counts, targets)
counts = tfidf_vectorizer.fit_transform(test['text'].values).toarray()
predictions = classifier.predict(counts)
EDIT: I have added the code snippet where I am only creating a tfidf vectorizer and using it to traing the classifier.
Like the previous comments suggest, a more specific question would result in a better answer, but I use this package all the time so I will try and help.
I. Determining top features for classification classes in sklearn really depends on the individual tool you are using. For example, many ensemble methods (like RandomForestClassifier and GradientBoostingClassifer) come with the .feature_importances_ attribute which will score each feature based on its importance. In contrast, most linear models (like LogisticRegression or RidgeClassifier) have a regularization penalty which penalizes for the size of coefficients, meaning that the coefficient sizes are somewhat a reflection of feature importance (although you need to keep in mind the numeric scales of individual features) which can be accessed using the .coef_ attribute of the model class.
In summary, almost all sklearn models have some method to extract the feature importances but the methods are different from model to model. Luckily the sklearn documentation is FANTASTIC so I would read up on your specific model to determine your best approach. Also, make sure to read the User Guide associated with your problem type in addition to the model specific API.
II. There is no out of the box sklearn method to provide the mis-classified records but if you are using a pandas DataFrame (which you should) to feed the model it can be accomplished in a few lines of code like this.
import pandas as pd
from sklearn.linear_model import RandomForestClassifier
df = pd.DataFrame(data)
x = df[[<list of feature columns>]]
y = df[<target column>]
mod = RandomForestClassifier()
mod.fit(x.values, y.values)
df['predict'] = mod.predict(x.values)
incorrect = df[df['predict']!=df[<target column>]]
The resultant incorrect DataFrame will contain only records which are misclassified.
Hope this helps!
I have a time-series forecasting problem that I am using the statsmodels python package, I applied the ARIMA MODEL, In python sm.tsa.ARIMA(data, (p,1,q)) usually transform the data to the first different, for example if we have a raw data (y1,y2,y3,y4....), first thing ARIMA Find the first difference,(y1-y2,y2-y3,....), so it make the model from this new data (first difference data). my question when I found the model
arma_mod1=sm.tsa.ARIMA(firstdifference, (p,1,q))
I can predict the first difference data as follow
predict_oil =arma_mod11.predict('1980', '2026').
MY QUESTION: How can I predict the future raw data ( the main data not the first difference data) using Arima?
Thanks
The predict method takes an optional parameter named typ which lets you decide whether to have predictions in the original time series or in the differenced one.
You should use
predict_oil =arma_mod11.predict('1980', '2026', typ='levels')
I don't think this will be still helpful for you, but maybe it will be for others.