A Practical Approach to Timeseries Forecasting Using Python - Graph for TSF Using LSTM

The video tutorial covers the process of converting forecast data into date format and plotting it using the SNS library. It demonstrates how to visualize both the original and forecasted data. The tutorial also discusses the results of an LSTM model with 32 neurons and explores the effects of changing LSTM parameters like epochs and the number of LSTMs on the results.

What is the first step in handling the date column in the forecast data?

• Remove the date column

• Convert it into a date format

• Add a new date column

• Convert it into a string format

Which library is used for plotting the data in this tutorial?

• Matplotlib

• NumPy

• SNS

• Pandas

What is the figure size set for the plots in the tutorial?

• 10x5

• 15x8

• 5x3

• 20x10

What does the LSTM model use to predict future dates?

• 8 neurons

• 64 neurons

• 32 neurons

• 16 neurons

What is plotted against the original data in the LSTM model results?

• Random dates

• Past dates

• Current dates

• Future dates

What changes are explored in the LSTM model in the final section?

• Changing the output format

• Changing the input data

• Changing the data type

• Changing epochs and number of LSTMs

What is the purpose of modifying the LSTM model?

• To increase the data size

• To observe changes in results

• To decrease the processing time

• To simplify the model