Employing Deep Learning and Time Series Analysis to Tackle the Accuracy and Robustness of the Forecasting Problem

Abstract

Crime is a bone of contention that can create a societal disturbance. Crime forecasting using time series is an efficient statistical tool for predicting rates of crime in many countries around the world. Crime data can be useful to determine the efficacy of crime prevention steps and the safety of cities and societies. However, it is a difficult task to predict the crime accurately because the number of crimes is increasing day by day. The objective of this study is to apply time series to predict the crime rate to facilitate practical crime prevention solutions. Machine learning can play an important role to better understand and analyze the future trend of violations. Different time-series forecasting models have been used to predict the crime. These forecasting models are trained to predict future violent crimes. The proposed approach outperforms other forecasting techniques for daily and monthly forecast.

Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia

“…A typical autoregressive model takes previous values and uses a linear combination of those values to forecast the future values of the variable of interest. The moving-average model employs the previous forecasts' errors in a manner similar to that of a regression model [35]. ARIMA provides realistic results when the data show no seasonality [9,22].…”

“…The general notation for ARIAM is ARI MA(p, d, q), where p is used to calculate AR using p preceding periods from the time series, d represents the degree of differencing that is used to transform the data into a stationary series, and q is the order of the moving average. Forecasting using ARIMA is calculated as follows [35]: ARIMA and exponential smoothing, which are the most used models, are only capable of handling one seasonality. Varying seasonal trends are often seen in time series (e.g., hourly data that contain a daily, weekly, and annual pattern).…”

Automating Time Series Forecasting on Crime Data using RNN-LSTM

“…When it comes to sequential data or ordered data in which data points are interdependent, these NN are a greater disadvantage. Here RNN [4]come into picture which could be imagined as they have a sense of memory to remember what happened in the past. Hence RNN is best suited for time series analysis.…”

“…The major challenge of this research is to handle the growing volumes of crime data and to build a predictive model with improved accuracy. This paper is intended to build a better performing forecasting model [4] [6] on the crime data. The objective of the current study is to gage the forecasting capacity of the NBeats model [1] on crime data, which is a hybrid of RNN-LSTM [4].…”

“…This paper is intended to build a better performing forecasting model [4] [6] on the crime data. The objective of the current study is to gage the forecasting capacity of the NBeats model [1] on crime data, which is a hybrid of RNN-LSTM [4]. This model will aid police personnel in optimal decisionmaking and resource management.…”