Now that we’ve covered the basics of time series analysis, I’m prepared to discuss how I’m using it for our BPDA economic indicators project.
Finding patterns and trends in Boston’s economy throughout time is my main objective. I’m doing this by examining data points like hotel occupancy rates, employment rates, housing costs, and traffic at Logan Airport. We can see how these data points have changed over time and spot any reoccurring trends by looking at them in chronological order.
This is how I’m going about it: I start by scanning the dataset for trends. This entails determining whether specific economic indicators have been rising, falling, or remaining largely stable over time. For instance, I might discover that there has been an upward trend in hotel occupancy rates.
trend in the summer, signifying the height of traveler activity.
I’m now looking at seasonality. Finding patterns that recur on a regular basis is required for this. Do certain months always see an increase in property prices? Is there a specific season of the year when employment peaks? Comprehending these periodic patterns might be essential for organizing and formulating policies.
Additionally, I’m watching for any anomalies or distinct data points that deviate from the norm. These could be indicators of unusual occurrences or shifts in Boston’s economic landscape.