Hello,
In this section, I conducted Time Series Decomposition for two key economic indicators: Hotel Occupancy Rates and Logan Airport Passengers. Here’s a breakdown of the process and the results:
– I created a combined date column by merging the “Year” and “Month” columns to create a datetime column. This datetime column was set as the index for the dataset, making it easier to perform time series analysis.
– I selected two economic indicators for analysis: Hotel Occupancy Rates and Logan Airport Passengers.
– For each indicator, I performed seasonal decomposition using the `seasonal_decompose` function from `statsmodels.tsa.seasonal`. I used an additive model and specified a period of 12 months, indicating that the seasonality repeats annually.
I plotted the decomposition results for both indicators, with each plot displaying four components:
1. Observed Data: This plot shows the actual values of the economic indicator over time, in this case, Hotel Occupancy Rates and Logan Airport Passengers.
2. Trend Component: The trend plot reveals the underlying trend or pattern in the data. It helps identify whether the indicator is generally increasing, decreasing, or following a specific pattern.
3. Seasonal Component: The seasonal plot displays the recurring patterns or seasonality in the data. It helps identify any regular fluctuations that occur at specific times of the year.
4. Residuals: The residuals plot represents the remaining variation in the data after removing the trend and seasonal components. It can provide insights into irregularities or unexpected changes in the data.
Formatting and Visualization:
– To enhance readability, I formatted the x-axis of the plots to display years using the `mdates` module. This makes it easier to identify trends and seasonality over time.
Interpretation:
– Time series decomposition is a valuable technique for understanding the underlying patterns and components within economic indicators. It allows us to separate the data into its constituent parts, making it easier to identify trends, seasonality, and irregularities.
– By examining the decomposition plots, we can gain insights into how Hotel Occupancy Rates and Logan Airport Passengers vary over time. This information can be crucial for making informed decisions and strategic planning in various sectors, including tourism and hospitality.
Overall, time series decomposition is a powerful tool for uncovering meaningful patterns within economic data, enabling better analysis and forecasting.