本次英国代写主要为时间序列相关
MAT005 Coursework 2021
救护车服务提供商雅加达想了解可能未来对救护车服务的需求在城市。
他们提供了5个月的数据
（2019年1月至2019年5月）。
他们对什么预测感兴趣？
•数据可在“学习中心”中找到（20 / 21-MAT005时间序列和预测下的
评估部分）在名为TimeSeriesCourseworkData20_21.xls的Excel电子表格中。
••“数据”工作表列出了呼叫的日期，时间以及进行呼叫的城市
起源。数据涵盖第1次之间的时间段
2019年1月和2019年5月31日。
••使用数据预测第1次之间的通话数量和方式
2019年6月– 2019年6月7日。如果
您将能够准确地进行进一步的预测，然后再做。
•他们希望以A3海报的形式与同事共享。
您需要在分析中做什么
•对数据进行初步分析，包括两者
数字和图形摘要。
•检查时间序列的组成部分：
基本趋势，季节性和错误以及产生
分解图。
•研究选择的时间序列模型以查看
哪种模型非常适合观察到的
数据。
•基准和简单方法，包括：天真，
均值，移动平均线，简单线性回归。
•复杂的方法，包括：SES，Holt Linear，Holt
温特斯，多元线性回归，ARIMA。
•请记住包括适当的错误统计信息
和每个预测的图形比较
模型。
The providers of ambulance services in
Jakarta want to understand the likely
future demand for ambulance services
in the city.
They have provided 5 months of data
(January to May 2019).
What forecasts are they interested in?
• The data is available on Learning Central (20/21-MAT005 Time Series and Forecasting under the
Assessment section) within an Excel spreadsheet called TimeSeriesCourseworkData20_21.xls.
• •The Data worksheet lists the date, time of call and the city municipality from which the call
originated. The data covers the time period between 1st
January 2019 and 31stMay 2019.
• •Use the data to predict the number and pattern of calls between 1st
June 2019 – 7th June 2019. If
you are able to accurately predict further then please do.
• They would like this in the form of an A3 poster they can share with their colleagues.
What do you need to do in your analysis
• A preliminary analysis of the data including both
numerical and graphical summaries.
• Examine the components of the time series: the
underlying trend, seasonality and error and produce
a decomposition plot.
• Investigate a selection of time series models to see
which model provides a good fit to the observed
data.
• Baseline & simple approaches, including: Naïve,
Mean, Moving Average, Simple Linear Regression.
• Complex approaches including: SES, Holt Linear, Holt
Winters, Multiple Linear Regression, ARIMAs.
• Remember to include the appropriate error statistics
and graphical comparisons for each forecasting
model.