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Statistical Analysis of Time Series and Forecasting

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  • Pergamenshchikov Serguei

    (LMRS - Laboratoire de Mathématiques Raphaël Salem - UNIROUEN - Université de Rouen Normandie - NU - Normandie Université - CNRS - Centre National de la Recherche Scientifique)

  • Pchelintsev Evgeny

    (SSP&QF - International Laboratory of Statistics of Stochastic Processes and Quantitative Finance - Tomsk State University [Tomsk])

Abstract

In this course, we present the principal parts of the time series analysis. First, stationary processes and trends in times series are introduced. Then we consider the linear regression models for which we study the main problems such that point estimation, the construction of confidence intervals, hypothesis testing, and forecasting. In addition, big data models and the main methods for their analysis are presented. Finally, we introduce the autoregressive and moving average autoregressive processes (ARMA) and study their basic properties, including the problem of forecasting.

Suggested Citation

  • Pergamenshchikov Serguei & Pchelintsev Evgeny, 2023. "Statistical Analysis of Time Series and Forecasting," Working Papers hal-03969254, HAL.
    • Handle: RePEc:hal:wpaper:hal-03969254
    Note: View the original document on HAL open archive server: https://hal.science/hal-03969254
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