MY-ASSIGNMENTEXPERT™可以为您提供lse.ac.uk ST304 Time Series & Prediction时间序列和预测课程的代写代考和辅导服务！
这是伦敦政经学校时间序列和预测课程的代写成功案例。
ST304课程简介
Teacher responsible
Dr Yining Chen COL 7.06
Availability
This course is available on the BSc in Actuarial Science, BSc in Business Mathematics and Statistics, BSc in Data Science, BSc in Mathematics with Economics and BSc in Mathematics, Statistics and Business. This course is available as an outside option to students on other programmes where regulations permit and to General Course students.
This course is not capped, any student that requests a place and meet the criteria will be given one.
Prerequisites
Pre-requisites
2nd year statistics and probability
Students who have no previous experience in R are required to complete an online pre-sessional R course from the Digital Skills Lab before the start of the course (https://moodle.lse.ac.uk/course/view.php?id=7745)
Course content
The course introduces the student to the statistical analysis of time series data and simple time series models, and showcase what time series analysis can be useful for. Topics include: autocorrelation; stationarity, trend removal and seasonal adjustment; AR, MA, ARMA, ARIMA; estimation; forecasting; unit root test; introduction to financial time series and the ARCH/GARCH models; basic spectral analysis. The use of R for time series analysis will also be covered.
ST304 Time Series & Prediction HELP（EXAM HELP， ONLINE TUTOR）
(1) Estimate the trend of the series of gasoline consumption in Spain using a straight line in the period from 1945 to 1995 and generate forecasts for 24 months. Compare the results with Holt’s method.
(2) Apply a decomposition method to the gasoline consumption series and estimate the seasonal coefficients. Compare the results when Holt’s method and using moving averages.
(1) To estimate the trend of the series of gasoline consumption in Spain using a straight line, you can apply a simple linear regression model where gasoline consumption is the dependent variable, and time (measured in years) is the independent variable. The estimated slope of the regression line represents the trend in gasoline consumption, and the intercept represents the level of consumption at the start of the period. You can use this model to generate forecasts for 24 months by extrapolating the regression line. Holt’s method is a forecasting method that takes into account both the trend and the seasonality of the data. It is a more advanced method than a simple linear regression, and it may provide better forecasts, especially if the data exhibits seasonality.
(2) The decomposition method is a time series analysis technique that separates a time series into its underlying components: trend, seasonality, and random fluctuations. To estimate the seasonal coefficients, you can use either the moving averages method or the Holt’s method. In the moving averages method, you calculate the average of the data over the same season for several years. In Holt’s method, you estimate the seasonal coefficients along with the trend and the level of the data using an iterative procedure that minimizes the sum of squared errors.
(3) Obtain the periodogram for the series of Columbus’s voyage and interpret it.
(4) Obtain the periodogram for the gasoline consumption series and interpret it.
(3) The periodogram is a graphical representation of the spectral density of a time series. It shows the distribution of power across different frequencies in the data. If you obtain the periodogram for the series of Columbus’s voyage, you can identify the dominant frequencies in the data and interpret them in terms of seasonal or other patterns.
(4) To obtain the periodogram for the gasoline consumption series, you can apply a Fourier transform to the data. The periodogram shows the distribution of power across different frequencies in the data. If you obtain the periodogram for the gasoline consumption series, you can identify the dominant frequencies in the data and interpret them in terms of seasonal or other patterns.
(5) Prove that the predictions made using Holt’s method verify the recursive equation- $\hat{z}_{T+1}(1)=\hat{z}_T(1)+$ $\widehat{\beta}_T+\gamma(1-\theta)\left(z_{T+1}-\widehat{\mu}_T-\widehat{\beta}_T\right)$
(5) The recursive equation you provided is the update equation for Holt’s linear exponential smoothing method. It shows how the forecast for the next period depends on the forecast for the current period and the current observation. The equation includes the smoothing parameters alpha, beta, and gamma, which control the weights given to the level, trend, and seasonal components of the data. The equation is derived by minimizing the sum of squared errors between the observed values and the forecast values. You can prove that the equation holds by using the definitions of the smoothing parameters and the update equations for the level, trend, and seasonal components.
MY-ASSIGNMENTEXPERT™可以为您提供LSE.AC.UK ST304 TIME SERIES & PREDICTION时间序列和预测课程的代写代考和辅导服务！