# Sequences, Time Series and Prediction Coursera Quiz Answers

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 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

Question 10: 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 11: What’s the input shape for a univariate time series to a Conv1D?

• []
• [None, 1]
• [1]
• [1, None]

Question 12: 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 13: 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?

Question 14: 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?

• 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 15: What was the sunspot seasonality?

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

Question 16: 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 17: 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

Question 18: What is an example of a Univariate time series?

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

Question 19: What is an example of a Multivariate time series?

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

Question 20: 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 21: A sound wave is a good example of time series data

• False
• True

Question 22: 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 23: 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 24: 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 25: 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 26: 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

Question 27: 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 28: 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 29: 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 30: 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 31: 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 32: 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 33: 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 34: 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

A time series is a collection of data points that appear in a specific order over a period of time. A time series in investing follows the movement of selected data points, such as the price of a securities, over a set period of time, with data points recorded at regular intervals.
Seeing if one of your axes is time is an easy method to tell if the dataset you’re dealing with is time series or not.
A time series is a collection of images taken at evenly spaced intervals over a period of time. As a result, it’s a series of discrete-time data. Ocean tidal heights, sunspot counts, and the Dow Jones Industrial Average’s daily closing value are all examples of time series.