Month: December 2023

In “Boston’s Economic Diary,” we embark on a comprehensive journey through the economic heartbeat of Boston from 2013 to 2019. This detailed study utilises a spectrum of data to paint a vivid picture of the city’s economic growth and challenges during this period.

Key Highlights of the Analysis:

• Sectoral Interplay and Economic Health: An exploration into how varying sectors, like Boston Logan International Flights and Passengers, and Hotel Occupancy, interconnect and influence the city’s economic vitality.
• Airport Dynamics and Job Market Trends: A deep dive into the increasing trend of Logan Airport passengers and how this surge correlates with employment growth in the city.
• Hotel Industry’s Economic Indicators: An analysis of Hotel Average Daily Rates and Occupancy, revealing their relationship with Boston’s broader economic trends.
• Predictive Economic Modeling: Advanced techniques, including neural network modelling, shed light on future economic trends based on key indicators like airport traffic and hotel industry data.

For a comprehensive understanding of our insights and their broader impact, you can access the full report.

Project_Report#3

 

 

Working on time series analysis in the economy indicators dataset, I probably uncovered the patterns of Hotel Average Rate variables. In my analysis, I have successfully done the Autocorrelation Function (ACF) and Partial Autocorrelation (PACF).

Autocorrelation Function (ACF):

• The ACF measures the correlation between a time series and its lagged values, or earlier data.
• It shows the general dependence structure of the time series and computes the correlation coefficients for various lags.
• Finding the order of a moving average (MA) process can be done with the help of the ACF. Compared to other kinds of processes, the autocorrelations in an MA process drop more slowly.

While working on ACF for building a pre-model, I got the plot for the Hotel average rate. The number of lags to include in the plot is determined by the ‘lag’ parameter, which you can change to suit your needs. The width of the confidence interval surrounding the autocorrelation values is determined by the ‘alpha’ parameter.

 

Partial Autocorrelation Function (PACF):

• In contrast, the PACF eliminates the impact of the intermediate lags from the measurement of the correlation between a time series and its lagged values.
• By removing the influence of the intermediate lags, it aids in determining the direct relationship between observations at various lags.
• When determining the sequence of an autoregressive (AR) process, the PACF is especially helpful. For lags longer than the process order, the partial autocorrelations in an AR process go to zero.

Same as ACF, just need to update the library and I performed the Partial Autocorrelation Function (PACF) for the both the variables.

The below graphs determined ACF & PACF:

 

 

In this blog, I’ll provide an overview of time series analysis, covering its objectives, the analysis process, and its limitations.

**What is Time Series Analysis?**
Time series analysis involves examining data points gathered regularly over a defined period, offering insights into how variables change over time.

**Objectives of Time Series Analysis:**
The goals include understanding the dynamics of time series variables, gaining insights into changing dataset features over time, and facilitating predicting of future values.

**How to Analyze Time Series:**
1. Gather and clean data.
2. Visualize time versus key features.
3. Assess series stationarity.
4. Create charts to understand characteristics.
5. Utilize ARMA, ARIMA, MA, and AR models.
6. Conclude forecasts.

**Limitations of Time Series Analysis:**
1. Does not handle missing data.
2. Assumes linear relationships between data points.
3. Data transformations can be resource-intensive.
4. Primarily operates on univariate data.

In delving deeper into time series analysis, I’ve explored topics such as autocorrelation, forecasting, and cyclical analysis. In considering regression analysis, I’ve concluded that conventional linear regression may not be ideal for time series research, especially when dealing with data exhibiting seasonality, trends, or temporal dependencies. Time series regression is better approached with techniques like autoregressive integrated moving average (ARIMA) models, which account for the inherent characteristics of time series data.

Autocorrelation Function (ACF): A statistical tool, ACF assesses the correlation between a time series and its own lagged values. It aids in identifying patterns, trends, and seasonality by displaying correlation coefficients for different lags, highlighting significant delays and autocorrelation patterns.

Forecasting: The forecasting process often begins with using the most recent observed value from historical data. Iterative prediction involves projecting subsequent observations based on the assumption that changes or deviations are random and unpredictable.

Evaluation: Performance assessment of the forecasting model is done using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE). These metrics compare projected values to actual observations, providing insights into the model’s accuracy.