Exploring New Dimensions in Time Series Forecasting: Predictive Insights from Police Analysis Data

Case Study: Police Analysis Data

Let’s consider a hypothetical case study of police analysis data. This data isn’t just about the count of incidents. It encompasses various metrics such as the type of incidents, geographical locations, time of the day, response times, and outcomes. By applying time series forecasting to these different values, we can predict several aspects

The analysis of the police shooting data in the PDF provided offers some interesting insights from a time series perspective. Here’s a summary of the results:

1. Data Collection: The data includes 8,002 shooting dates and 2,702 unique dates, reflecting police shooting incidents. This data was then used to count the number of shootings that occurred on each unique date.
2. Initial Observations: The initial analysis involved plotting the sequence of counts (number of shootings per day). This provided a basic understanding of how the frequency of these incidents varied over time.
3. Monthly Analysis: To gain a broader perspective, the daily counts were replaced with monthly counts. This step helped in visualizing longer-term trends and patterns in the data.
4. Stationarity Test: A key part of time series analysis is determining if the data is stationary, meaning its statistical properties like mean and variance are constant over time. The document details the use of a unit root test, which indicated that the original time series data was likely not stationary. However, after differencing the data (calculating the difference between consecutive data points), the resulting series appeared to be stationary.
5. Model Fitting: The analysis applied Mathematica’s TimeSeriesModelFit function, fitting an autoregressive moving-average (ARMA) model to the differenced data. This model helps in understanding the underlying patterns and can be used for forecasting.
6. Predictions and Forecasting: The primary goal of time series analysis is to forecast future values. The document does not explicitly mention specific forecasts or predictions made from this data, but the methodologies and models applied are typically used to predict future trends based on historical data.

The analysis demonstrates the application of time series forecasting methods to a real-world dataset, highlighting the importance of checking for stationarity, transforming data accordingly, and fitting appropriate models for analysis and forecasting.