# Sequences, Time Series and Prediction Coursera Quiz Answers

## All Weeks Sequences, Time Series and Prediction Coursera Quiz Answers

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.

## Sequences, Time Series and Prediction Coursera Quiz Answers

**Week 1 Quiz Answers: **

**Sequences, Time Series and Prediction**

Q1: What is an example of a Univariate time series?

- Hour by hour weather
- Baseball scores
- Fashion items
**Hour by hour temperature**

Q2: What is an example of a Multivariate time series?

- Baseball scores
- Hour by hour temperature
**Hour by hour weather**- Fashion items

Q3: 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

Q4: A sound wave is a good example of time series data

- False
**True**

Q5: 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

Q 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**

Q7: 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**

Q8: 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

Q9: 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

Q1: 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**

Q2: 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

Q3: 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]))

Q4: What does MSE stand for?

- Mean Slight error
**Mean Squared error**- Mean Series error
- Mean Second error

Q5: What does MAE stand for?

- Mean Average Error
- Mean Advanced Error
**Mean Absolute Error**- Mean Active Error

Q6: 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]

Q7: 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

Q8: 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

Q9: 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

Q1: 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

Q2: 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

Q3: 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**

Q4: 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**

Q5: 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

Q6: 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**

Q7: 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()

Q8: 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**

Q1: 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

Q2: What’s the input shape for a univariate time series to a Conv1D?

- []
**[None, 1]**- [1]
- [1, None]

Q3: 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

Q4: 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)**

Q5: 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])**

Q6: What was the sunspot seasonality?

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

Q7: 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**

Q8: 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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Course 04: Sequences, Time Series, and Prediction