Browsing by Subject "Time series data"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Anomaly detection of time series.
    (2010-05) Cheboli, Deepthi
    This thesis deals with the problem of anomaly detection for time series data. Some of the important applications of time series anomaly detection are healthcare, eco-system disturbances, intrusion detection and aircraft system health management. Although there has been extensive work on anomaly detection (1), most of the techniques look for individual objects that are different from normal objects but do not consider the sequence aspect of the data into consideration. In this thesis, we analyze the state of the art of time series anomaly detection techniques and present a survey. We also propose novel anomaly detection techniques and transformation techniques for the time series data. Through extensive experimental evaluation of the proposed techniques on the data sets collected across diverse domains, we conclude that our techniques perform well across many datasets.
  • Thumbnail Image
    Item
    Applied Time Series and Duluth Temperature Prediction
    (2017-06) Wan, Xiangpeng
    Autoregressive integrated moving average (ARIMA) has been one of the popular linear models in time series forecasting during the past three decades.The Triple Expo- nential Model also can be used to fit the time series data. This project takes Duluth temperature predictions as a case study, finding the best statistical model to predict the temperature. I collected 30 years of Duluth monthly maximum temperature data, from 1986 to 2016, and I fi t 29 years of them into di erent models including Triple Exponential Smoothing model, ARIMA model, and SARIMA model. Then I predicted the last year's temperature in those models, and I compared them to the true value of last year's temperature, which gave me the SSE value for each model so that I could find the best model.