Time Series Analysis and Forecasting

Assignment help is an online tutoring site which caters to all the needs and requirements of the students by providing guidance of professional tutors in various fields. One such subject is statistics in which the students can seek assistance of our expert tutors. In statistics the students can get help in various sub-disciplines like time series analysis and forecasting. This subject gives an introduction to time series models and associated methods of data analysis and inference. In statistics, signal processing, econometrics and mathematical finance, a time series is a sequence of data points, measured typically at successive times spaced at uniform time intervals. Examples of time series are the daily closing value of the Dow Jones index or the annual flow volume of the Nile River at Aswan. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to forecast future events based on known past events to predict data points before they are measured. Time series are very frequently plotted via line charts. Methods for time series analyses may be divided into two classes: frequency-domain methods and time-domain methods. The former include spectral analysis and recently wavelet analysis; the latter include auto-correlation and cross-correlation analysis.

The course includes study of auto regressive (AR), moving average (MA), ARMA and ARIMA processes, stationary and non-stationary processes, seasonal processes, auto-correlation and partial auto-correlation functions, identification of models, estimation of parameters, diagnostic checking of fitted models, forecasting, spectral analysis, and transfer function models.

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