Introductory Time Series Analysis Textbook

In summary, the conversation discusses the search for a good textbook on time series analysis for an upcoming course. The list of topics to be covered includes trend analysis, trend-based methods, stationary processes, ARMA and ARIMA models, spectrum estimation, frequency filtration, seasonal models, multivariate models, and the Kalman filter. The Wei textbook is recommended for its comprehensive coverage and use of software examples. The Shumway and Stoffer textbook is also mentioned as a good resource for time series analysis, and Dan Simon's book is recommended for the Kalman filter. The conversation concludes with the offer for further assistance and information from the Autobox software company.
  • #1
hsu
2
0
I'll be taking an introductory course on time series analysis in the spring, and we will be using the instructor's online notes as the "textbook". My previous experiences with such instructor's notes have been that they contain only the essentials of the course and aren't really useful as references. Besides, I always like using a physical textbook.

So, can anyone recommend a good textbook for this subject that is both clear for learning from and useful as a reference? The list of topics to be covered is:

Trend analysis, trend-based methods (moving averages, exponential smoothing), stationary processes, ARMA and ARIMA models, spectrum and its estimation, frequency filtration, seasonal models, multivariate models, Kalman filter.

By the way, another professor who sometimes teaches this course at my university has used this textbook:

https://www.amazon.com/dp/0321322169/?tag=pfamazon01-20

in the past; would that be one to consider?

Thanks!
 
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  • #2
I liked Shumway and Stoffer for time series, but for the Kalman filter Dan Simon's book is exceptionally friendly.
 
  • #3
Hsu,

The Wei textbook is excellent for a number of reasons. It describes what to look for that other textbooks don't address. Chapter 9 discussed outliers and how important they are in order to model the data (ie additive outliers(AO), innovative outliers(IO)). Example 9.5 uses our software (Autobox) in the example. It doesn't discuss level shifts (ie 0,0,0,0,1,1,1,1,etc) or seasonal pulses (ie 0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,etc for monthly data).

The transfer function models examples also used Autobox, but do not show the use of them with interventions as the book was written back in 1990 and did not include those advances over time (but what other textbook does? :) You can use Autobox as a student with ~700 time series from textbooks and of course Wei's.

See the ackowledgements section in the front for our name.

We would be happy to answer any questions here or off-line.

www.autobox.com

Regards
Tom Reilly
 
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1. What is time series analysis?

Time series analysis is a statistical method used to analyze and understand patterns in data that are collected over time. It involves studying the behavior of a variable or set of variables over a specific time period to identify trends, patterns, and relationships.

2. Why is time series analysis important?

Time series analysis is important because it allows us to make predictions and forecasts based on past data. It is widely used in various fields such as economics, finance, meteorology, and social sciences to understand and predict future trends and patterns.

3. What are the key components of a time series?

The key components of a time series are trend, seasonality, cyclical patterns, and irregular variations. Trend refers to the long-term pattern or direction of the data. Seasonality refers to recurring patterns that occur at regular intervals. Cyclical patterns are fluctuations that occur over a period of several years. Irregular variations are random fluctuations that cannot be explained by the other components.

4. What are some common methods used in time series analysis?

Some common methods used in time series analysis include moving average, exponential smoothing, autoregression, and ARIMA (autoregressive integrated moving average) models. These methods help to identify and quantify the different components of a time series and make predictions based on the data.

5. Is time series analysis only used for forecasting?

No, time series analysis can also be used for other purposes such as identifying patterns and relationships in the data, detecting outliers or anomalies, and evaluating the effectiveness of interventions or policies. It is a versatile tool that can provide valuable insights into a wide range of data sets.

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