Unable to get appropriate prediction using statsmodel for HoltWinters

Unable to get appropriate prediction using statsmodel for HoltWinters

By : user2172310
Date : October 24 2020, 08:10 AM
I think the issue was by ths following , The exponential smoothing model you've chosen doesn't include a trend, so it is forecasting the best level, and that gives a horizontal line forecast.
If you do:
code :
model = ExponentialSmoothing(data_train, trend='add')
# Simulate some data
dta = pd.Series(np.arange(100) + np.sin(np.arange(100)) * 5 + np.random.normal(scale=4, size=100))

# Perform exponention smoothing, no trend
mod1 = sm.tsa.ExponentialSmoothing(dta)
res1 = mod1.fit()
fcast1 = res1.forecast(30)

plt.plot(fcast1, label='Model without trend')

# Perform exponention smoothing, with a trend
mod2 = sm.tsa.ExponentialSmoothing(dta, trend='add')
res2 = mod2.fit()
fcast2 = res2.forecast(30)

plt.plot(fcast2, label='Model with trend')
plt.legend(loc='lower right')

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OLS using statsmodel.formula.api versus statsmodel.api

OLS using statsmodel.formula.api versus statsmodel.api

By : user3750430
Date : March 29 2020, 07:55 AM
will be helpful for those in need The difference is due to the presence of intercept or not:
in statsmodels.formula.api, similarly to the R approach, a constant is automatically added to your data and an intercept in fitted
code :
x1 = sm.add_constant(x1)
TensorFlow - Unable to get Prediction

TensorFlow - Unable to get Prediction

By : N.Dominix
Date : March 29 2020, 07:55 AM
I wish did fix the issue. I combined your train_neural_network and make_prediction function into one single function. Applying tf.nn.softmax to the model function would make the value range into from 0~1 (interpreted as probability), then tf.argmax extracts the column number with the higher probability. Note that the placeholder for y in this case needs to be one-hot-encoded. (If you are not one-hot-encoding y here, then pred_y=tf.round(tf.nn.softmax(model)) would convert the output of softmax into 0 or 1)
code :
def train_neural_network_and_make_prediction(train_X, test_X):

    model = neural_network_model(x)
    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(model, y) )
    optimizer = tf.train.AdamOptimizer().minimize(cost)

    ephocs = 10

    with tf.Session() as sess :
        for epoch in range(ephocs):
            epoch_cost = 0

            i = 0
            while i< len(titanic_train) :
                start = i
                end = i+batch_size
                batch_x = np.array( train_x[start:end] )
                batch_y = np.array( train_y[start:end] )

                _, c = sess.run( [optimizer, cost], feed_dict={x: batch_x, y: batch_y} )
                epoch_cost += c
            print("Epoch",epoch+1,"completed with a cost of", epoch_cost)
        # make predictions on test data
        predictions = pred_y.eval(feed_dict={x : test_X})
    return predictions
statsmodel ARMA in sample prediction

statsmodel ARMA in sample prediction

By : dharanjntu
Date : March 29 2020, 07:55 AM
wish helps you That's not really a meaningful plot or exercise.
You are accumulating one step-ahead forecasts that all start at different levels given by the history for that observation or at that time point.
Reproducing OLS prediction Python statsmodel

Reproducing OLS prediction Python statsmodel

By : Raymond de Jong
Date : March 29 2020, 07:55 AM
like below fixes the issue What I was missing was indeed quite simple and embarrassing. I switched 2 variable names around, resulting in wrong predictions manually. So, the formula was correct:
code :
y = a + B1 * X1 + B2 *X2 + .... Bn*Xn
Unable to make prediction on google automl

Unable to make prediction on google automl

By : user1678298
Date : March 29 2020, 07:55 AM
this one helps. The permission error messages are usually thrown when the application is not being authenticated correctly; Therefore, it is required to verify that the service account you are using has the required roles assigned, as well as provide the credentials to your application by using environment variables or explicitly point to your service account file in code. Keep in mind that when you set an environment variable value in a session, it is reset every time the session is dropped.
Additionally, AutoML Vision currently requires the location us-central1, as mentioned in the API Tutorial. Based on this, you should be fine on this aspect; However, you can take a look on the projects.locations REST methods in case you want to get additional information about this config.
code :
namespace Google\Cloud\Samples\Auth;

// Imports the Google Cloud Storage client library.
use Google\Cloud\Storage\StorageClient;

function auth_cloud_explicit($projectId, $serviceAccountPath)
    # Explicitly use service account credentials by specifying the private key
    # file.
    $config = [
        'keyFilePath' => $serviceAccountPath,
        'projectId' => $projectId,
    $storage = new StorageClient($config);

    # Make an authenticated API request (listing storage buckets)
    foreach ($storage->buckets() as $bucket) {
        printf('Bucket: %s' . PHP_EOL, $bucket->name());
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