Sequences, Time Series and Prediction Coursera Quiz Answers

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Week 1

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

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

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

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
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