A Practical Approach to Timeseries Forecasting Using Python - Seasonality Comparison

The video tutorial covers time series forecasting, focusing on comparing seasonality between different years. It explains how to set up graphs using RC parameters and analyze the seasonality of data from 2010 and 2014. The tutorial highlights differences in trends and the consistency of weekly spikes, emphasizing the importance of visual analysis in forecasting. The session concludes with a brief introduction to resampling for better data display.

What is the primary focus of the initial discussion in time series forecasting?

• Evaluating economic indicators

• Analyzing trends over a decade

• Comparing seasonality of different years

• Forecasting future sales

Which parameter is used for setting up the graph in the process of comparing seasonality?

• XY parameter

• Graph parameter

• Plot parameter

• RC parameter

What is the main difference observed between the seasonal graphs of 2010 and 2014?

• 2010 shows a smoother peak

• 2014 shows a smoother peak

• 2014 has more data points

• 2010 has more data points

What is the significance of the saw tooth formation in the 2010 data?

• It indicates a lack of seasonality

• It shows a smoother trend

• It conveys better information

• It suggests data inconsistency

What does the presence of four spikes every month in the seasonal data indicate?

• Inconsistent seasonality

• Yearly seasonality

• Weekly seasonality

• Monthly seasonality

How can large datasets be utilized in time series forecasting?

• By focusing only on recent data

• By using them for visual comparison and model improvement

• By ignoring seasonal patterns

• By discarding older data

What is the next topic to be discussed after seasonality?

• Forecasting accuracy

• Resampling

• Trend analysis

• Data cleaning