Sequences, Time Series and Prediction Coursera Quiz Answers | Get Free Coursera Verified Certificate in 2021

Hello Technocrats, Today we are going to share Sequences, Time Series and Prediction Certification Course Exam Answers. This certification course is totally free of cost✅✅✅ for you which is provided by Google.

Here, you will find Sequences, Time Series and Prediction Certification Exam Answers in Bold Color which are given below.

These answers are updated recently and are 100% correct✅ answers of all modules and final exam answers of Sequences, Time Series and Prediction Certification all Week Assessment Answers from Coursera.

Use “Ctrl+F” To Find Any Questions Answer. & For Mobile User, You Just Need To Click On Three dots In Your Browser & You Will Get A “Find” Option There. Use These Option to Get Any Random Questions Answer.

Apply Link – Sequences, Time Series and Prediction

Sequences, Time Series and Prediction Coursera Quiz Answers

Week 1 Quiz Answers: Sequences, Time Series and Prediction

Question 1: What is an example of a Univariate time series?

• Hour by hour weather  
• Baseball scores
• Fashion items
• Hour by hour temperature

Question 2: What is an example of a Multivariate time series?

• Baseball scores
• Hour by hour temperature
• Hour by hour weather
• Fashion items

Question 3: What is imputed data?

• A good prediction of future data
• A bad prediction of future data
• A projection of unknown (usually past or missing) data
• Data that has been withheld for various reasons

Question 4: A sound wave is a good example of time series data

• False
• True

Question 5: What is Seasonality?

• Data that is only available at certain times of the year
• A regular change in shape of the data
• Weather data
• Data aligning to the 4 seasons of the calendar

Question 6: What is a trend?

• An overall consistent flat direction for data
• An overall consistent downward direction for data
• An overall consistent upward direction for data
• An overall direction for data regardless of direction

Question 7: In the context of time series, what is noise?

• Sound waves forming a time series
• Data that doesn’t have a trend
• Data that doesn’t have seasonality
• Unpredictable changes in time series data

Question 8: What is autocorrelation?

• Data that follows a predictable shape, even if the scale is different
• Data that doesn’t have noise
• Data that automatically lines up in trends
• Data that automatically lines up seasonally

Question 9: What is a non-stationary time series?

• One that has a constructive event forming trend and seasonality
• One that has a disruptive event breaking trend and seasonality
• One that is consistent across all seasons
• One that moves seasonally

Week 2 Quiz Answers: Sequences, Time Series and Prediction

Question 1: What is a windowed dataset?

• A consistent set of subsets of a time series
• There’s no such thing
• The time series aligned to a fixed shape
• A fixed-size subset of a time series

Question 2: What does ‘drop_remainder=true’ do?

• It ensures that the data is all the same shape
• It ensures that all data is used
• It ensures that all rows in the data window are the same length by cropping data
• It ensures that all rows in the data window are the same length by adding data

Question 3: What’s the correct line of code to split an n column window into n-1 columns for features and 1 column for a label

• dataset = dataset.map(lambda window: (window[n-1], window[1]))
• dataset = dataset.map(lambda window: (window[:-1], window[-1:]))
• dataset = dataset.map(lambda window: (window[-1:], window[:-1]))
• dataset = dataset.map(lambda window: (window[n], window[1]))

Question 4: What does MSE stand for?

• Mean Slight error
• Mean Squared error
• Mean Series error
• Mean Second error

Question 5: What does MAE stand for?

• Mean Average Error
• Mean Advanced Error
• Mean Absolute Error
• Mean Active Error

Question 6: If time values are in time[], series values are in series[] and we want to split the series into training and validation at time 1000, what is the correct code?

time_train = time[:split_time]

x_train = series[:split_time]

time_valid = time[split_time:]

x_valid = series[split_time:]

time_train = time[split_time]

x_train = series[split_time]

time_valid = time[split_time:]

x_valid = series[split_time:]

time_train = time[:split_time]

x_train = series[:split_time]

time_valid = time[split_time]

x_valid = series[split_time]

time_train = time[split_time]

x_train = series[split_time]

time_valid = time[split_time]

x_valid = series[split_time]

Question 7: If you want to inspect the learned parameters in a layer after training, what’s a good technique to use?

• Run the model with unit data and inspect the output for that layer
• Decompile the model and inspect the parameter set for that layer
• Assign a variable to the layer and add it to the model using that variable. Inspect its properties after training
• Iterate through the layers dataset of the model to find the layer you want

Question 8: How do you set the learning rate of the SGD optimizer?

• Use the lr property
• You can’t set it
• Use the Rate property
• Use the RateOfLearning property

Question 9: If you want to amend the learning rate of the optimizer on the fly, after each epoch, what do you do?

• Use a LearningRateScheduler and pass it as a parameter to a callback
• Callback to a custom function and change the SGD property
• Use a LearningRateScheduler object in the callbacks namespace and assign that to the callback
• You can’t set it

Week 3 Quiz Answers: Sequences, Time Series and Prediction

Question 1: If X is the standard notation for the input to an RNN, what are the standard notations for the outputs?

• Y
• H
• Y(hat) and H
• H(hat) and Y

Question 2: What is a sequence to vector if an RNN has 30 cells numbered 0 to 29

• The Y(hat) for the first cell
• The total Y(hat) for all cells
• The Y(hat) for the last cell
• The average Y(hat) for all 30 cells

Question 3: What does a Lambda layer in a neural network do?

• Changes the shape of the input or output data
• There are no Lambda layers in a neural network
• Pauses training without a callback
• Allows you to execute arbitrary code while training

Question 4: What does the axis parameter of tf.expand_dims do?

• Defines the dimension index to remove when you expand the tensor
• Defines the axis around which to expand the dimensions
• Defines if the tensor is X or Y
• Defines the dimension index at which you will expand the shape of the tensor

Question 5: A new loss function was introduced in this module, named after a famous statistician. What is it called?

• Hubble loss
• Hawking loss
• Huber loss
• Hyatt loss

Question 6: What’s the primary difference between a simple RNN and an LSTM

• LSTMs have a single output, RNNs have multiple
• LSTMs have multiple outputs, RNNs have a single one
• In addition to the H output, RNNs have a cell state that runs across all cells
• In addition to the H output, LSTMs have a cell state that runs across all cells

Question 7: If you want to clear out all temporary variables that tensorflow might have from previous sessions, what code do you run?

• tf.cache.clear_session()
• tf.keras.backend.clear_session() 
• tf.keras.clear_session
• tf.cache.backend.clear_session()

Question 8: What happens if you define a neural network with these two layers?

tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),

tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),

tf.keras.layers.Dense(1),

• Your model will fail because you have the same number of cells in each LSTM
• Your model will fail because you need return_sequences=True after the first LSTM layer
• Your model will compile and run correctly
• Your model will fail because you need return_sequences=True after each LSTM layer

Week 4 Quiz Answers: Sequences, Time Series and Prediction

Question 1: How do you add a 1 dimensional convolution to your model for predicting time series data?

• Use a 1DConvolution layer type
• Use a Conv1D layer type
• Use a Convolution1D layer type
• Use a 1DConv layer type

Question 2: What’s the input shape for a univariate time series to a Conv1D?

• []
• [None, 1]
• [1]
• [1, None]

Question 3: You used a sunspots dataset that was stored in CSV. What’s the name of the Python library used to read CSVs?

• CommaSeparatedValues
• PyFiles
• CSV
• PyCSV

Question 4: If your CSV file has a header that you don’t want to read into your dataset, what do you execute before iterating through the file using a ‘reader’ object?

• reader.next
• reader.ignore_header()
• reader.read(next)
• next(reader)

Question 5: When you read a row from a reader and want to cast column 2 to another data type, for example, a float, what’s the correct syntax?

• float f = row[2].read()
• You can’t. It needs to be read into a buffer and a new float instantiated from the buffer
• Convert.toFloat(row[2])
• float(row[2])

Question 6: What was the sunspot seasonality?

• 11 years
• 11 or 22 years depending on who you ask
• 4 times a year
• 22 years

Question 7: After studying this course, what neural network type do you think is best for predicting time series like our sunspots dataset?

• RNN / LSTM
• DNN
• Convolutions
• A combination of all of the above

Question 8: Why is MAE a good analytic for measuring accuracy of predictions for time series?

• It punishes larger errors
• It biases towards small errors
• It only counts positive errors
• It doesn’t heavily punish larger errors like square errors do

Conclusion

Hopefully, this article will be useful for you to find all the Weeks and Final Quiz Answers of Sequences, Time Series and Prediction and grab some premium knowledge with less effort. If this article really helped you in any way then make sure to share it with your friends on social media and let them also know about this amazing training. You can also check out our other course Answers. So, be with us guys we will share a lot more free courses and their exam/quiz solutions also and follow our Techno-RJ Blog for more updates.