Books for self-studying time series analysis? I started by Time Series Analysis by Hamilton, but I am lost hopelessly. This book is really too theoretical for me to learn by myself. 
Does anybody have a recommendation for a textbook on time series analysis that's suitable for self-study? 
 A: Last year I started teaching introductory and semi-advanced time series course, so I embarked on journey of reading the (text-)books in the field to find suitable materials for students. Given that I did not find any post on CV, Quora or ResearchGate that would full satisfy me, I decided to share my conclusions here.
This text below lists several time series textbooks and provides their evaluation. The focus is on suitability of the textbook as introductory textbook, or their added value in case they are not suitable as introductory textbook.
Hamilton – Time Series Analysis
Probably the most famous time series textbook. And also probably the least suitable as introductory textbook of them all, despite often being recommended to students (including me): you must be either genius or insane (not mutually exclusive, obviously) to recommend this textbook to starting students. The textbook is very exhaustive and very rigorous, but this also makes it hard to read for those who are new to the topic. That said, this is the textbook everybody should know about – once you become serious about doing time series analysis (rather than just modelling) you will want to consult this book.
Enders – Applied time series
The best introductory textbook in this list. The books is especially strong in other than univariate topics, such as transfer function models, VARs, cointegration and non-linear models. Nevertheless its coverage of univariate models is still better than most. The book’s value comes from focus on intuition rather than technical exposition, extensive use of simple illustrative examples as well as more complicated real-world examples; all of this leaves you understanding when and why are given models used, and how do they work. Yet, despite not being technical, it still provides the right amount of technical material for the reader to see time series models as mathematical constructs they are.
Diebold - Elements of forecasting
While being introductory textbook for forecasting rather than time series, this book still manages to be the best intuitive introduction to time series modelling (as opposed to analysis – do not search for it there). Diebold has the unique ability to understand what people who don’t understand are likely not to understand. While it likely cannot serve as the sole textbook for time series course, it should be suggested as introductory reading to students – a book they want to read before they want to get serious studying time series. Major drawback is the limited scope of the book, which covers only univariate models.
Box, Jenkins - Time Series Analysis: Forecasting and Control
Probably most famous book dedicated to time series, from two pioneers of modelling time series. It should be stressed that their work and book is not solely focused on economics, which is a serious limitation for using this book as introductory textbook. Still, the book has its undisputable value in providing very detailed, and mostly digestible exposition of ARMA models. It should be consulted by those who have basic knowledge of time series but want to get deeper understanding of (mostly) univariate time series models.
Pankratz - Forecasting with Dynamic Regression Models
If you want to learn about multivariate single equation models, this is the book. The exposition is very digestible but at the same time provides sufficient technical detail. Moreover, it includes large number of very detailed examples that help reader understand the material.
Brooks - Introductory Econometrics for Finance
This is a great introductory textbook with focus on finance applications. The textbook is on the low end of the technical apparatus and as such it reads well. Moreover, it provides ample illustration of the theory, so that the basic concepts sink in well. Overall, it is recommended for courses that avoid the technicalities to focus on the intuition, but as such it cannot be the last textbook one reads before going out in the real world.
Tsay - Analysis of Financial Time Series
This book is sometimes feels like in-between. In most cases it is too technical for most starting students, but at moments it is able to suitably simplify difficult material – for example it contains the most digestible introduction to Kalamn filter mechanics. It should be recommended as textbook for students that have some basic knowledge of time series models and what to get deeper into the topic with focus on financial time series.
Harvey – Time series models
This textbook provides very digestible mix of intuition and theory when presenting standard time series models and methods. From the perspective of modern reader the list of models and sequencing of their exposition is somewhat outdated, but for each type of model (ARMA, unobserved components, …) it provides exposition that is illuminating to beginners and advanced readers alike. Still, I would recommend this textbook as something you read after you read more introductory textbook.
Harvey – Elements of Analysis of Time Series
This textbook is best thought as complementary to ‘Time series models’ by the same author. It goes into the details of estimation techniques of different econometrical models, including the workings of algorithms and underlying statistical theory. That means that for the question of “what&why happens after I click estimate” it is unparalleled resource. In addition the chapters on multivariate single equation time series models provide very useful exposition of these models.
Harvey – Forecasting, structural time series models and the Kalman filter
This is an in-depth textbook on structural models and Kalman filter. As such it goes further than probably most readers will want to go. However, the introductory chapters are written with the usual great mix of intuitive and technical approach typical of the author. More than recommended for the start of using Kalman filter.
Maddala and Kim - Unit Roots, Cointegration, and Structural Change
This is probably the book on unit roots and cointegration, but one should be aware how to use this book. The best way to think about this book is as a textbook for advanced reader on relevant topics; but it will not serve well to beginners. Assuming one is knowledgeable enough then reading this book will be extremely beneficial. An especially good features of the book are (1) inclusion of historical narrative which allows the reader to orient himself in the literature, (2) encyclopedical approach to existing statistical tests combined with audacity to evaluate alternative tests,  (3) intuitive introduction to Winer process theory (much more digestible than Hamilton) underlying much of the econometrics of integrated processes.
Banerjee et al - Co-Integration, error correction, and the econometric analysis of non-stationary data
This is not a textbook, but it is a useful source for some specific topics. It can serve as very good advanced introduction to econometrics of integrated processes, including the unit roots. It has great introduction to error-correction models in its multiple representations, which is useful to anybody interacting with multivariate single equation models. And finally, it provides the reconstruction of academic research on co-integration as it was in 1991, eliminating the need to go into the actual papers.
A: In addition to the other text there are two books introductory books in Springer's Use R! series that cover time series:
Introductory Time Series with R
and
Applied Econometrics in R
There is also an advanced econometrics text in the series,
Analysis of Integrated and Co-integrated Time Series with R.
I have not used these but have found several others in the series to be excellent.
A: Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos is available free online.
It's a good book in its own right; Hyndman's previous forecasting book with Makridakis and Wheelright is highly regarded, but this has the added advantage that you can see what you're getting for the price.
A: There are three books that I keep referring to always from an R programming and time series analysis perspective:

*

*Time Series Analysis and Its Applications: With R Examples by
Shumway and Stoffer

*Time Series Analysis: With Applications in R by Cryer and Chan.

*Introductory Time Series with R by Cowpertwait and Metcalfe

The first book by Shumway and Stoffer has an open source (abridged) version available online called EZgreen version.
If you are specifically looking into time series forecasting, I would recommend following books:

*

*Forecasting Methods and Applications by Makridakis, Wheelwright and Hyndman. I keep referring to this book repeatedly, This is a classic, writing style is absolutely phenomenal.

*An online successor to the above book with nice R examples is Forecasting Principles and Practice by Hyndman and Athana­sopou­los.

*If you are looking at classic Box Jenkins modeling approach, I would recommend Time Series Analysis: Forecasting and Control by Box, Jenkins and Reinsel.

*An exceptional treatment on transfer function modeling and forecasting is in
Forecasting with Dynamic Regression Models by Pankratz. Again the writing style is absolutely great.

*Another extremely useful if you in to applying forecasting to solve
real world problems is Principles of Forecasting by Armstrong.

In my opinion, books 1, 4 and 5 are some of the best of the best books. Many like Forecasting Principles and Practice by Hyndman and Athana­sopou­los because it's open source and has R codes. It is no way closer to the breadth, the depth of coverage of forecasting methods and the writing style of its predecessor Makridakis et al.. Below are some contrasting features on why I like the Makridakis et al:

*

*List of references: for instance in the Box Jenkins chapter Makridakis et al has ~31 references, Hyndman et al there is very little or no references in many chapters.

*Breadth and Depth in coverage - Hyndman et al. mainly focus on Univariate methods especially developed by the first author, while Makridakis et. al focus not just on their own research but a wide variety of methods and application and also emphasis is on real world application and learning as opposed to being more academically focused.

*Writing style - I really cant complain as both the books are
exceptionally well written. However I personally lean towards Makridakis because it boils down complex concepts into reader friendly sections. There is a section on Dynamic regression or transfer functions, I have no where encountered such clear explanation on this "complex method". It takes extraordinary writing talent to help reader understand what Dynamic regression is in 15 pages and they succeed at it.

*Makridakis et al is software/method agnostic and they list some useful software packages and compare and contrast them (although this is almost 20 years old) is still a very valuable for a practitioner.

*Three dedicated chapters on how to apply forecasting in real world in Makridakis et al. which is big plus to have for a practitioner.

Forecasting is simply not running univariate methods like arima and exponential smoothing and producing outputs. It is much more than that, and especially strategic forecasting when you are looking into longer horizon. Principles of forecasting by Armstrong goes beyond the univariate extrapolation methods and is highly recommended for anyone who does real world forecasting especially strategic forecasting.
A: There are some good, free, online resources:


*

*The Little Book of R for Time Series, by Avril Coghlan (also available in print, reasonably cheap) - I haven't read through this all, but it looks like it's well written, has some good examples, and starts basically from scratch (ie. easy to get into).

*Chapter 15, Statistics with R, by Vincent Zoonekynd - Decent intro, but probably slightly more advanced. I find that there's too much (poorly commented) code, and not enough explanation thereof. 

A: If you find Hamilton too difficult then there is Econometric Modeling: A Likelihood Approach (Princeton Uni Press) by Bent Nielsen and David Hendry. It focuses more on intuition and practical how-tos than deeper theory. So if you're on a time constraint then that would be a good approach.
I would still recommend to persevere with Time Series Analysis by Hamilton. It is very deep mathematically and the first four chapters will keep you going for a long time and serve as a very strong introduction to the topic. It also covers Granger non-causality and cointegration and if you decide to pursue this topic more deeply then it is in invaluable resource.
For a more intuitive treatment of cointegration, I would also recommend Cointegration, Causality, and Forecasting by Engle and White.
Finally for very advanced treatments, there is Soren Johansen's book "Likelihood-Based Inference in Cointegrated VARs" and of course David Hendry's "Dynamic Econometrics".
Among those two, I would think Hendry's is more big-picture oriented and Johansen is pretty hard-going on the math.
A: In my opinion, you really can't beat Forecasting: principles and practice.  It's written by CV's own Rob Hyndman and George Athana­sopou­los, it's available for free online, and it's got tons of example code in R, making use of the excellent forecast package.
A: I would recommed the following books:

*

*Time Series Analysis and Its Applications: With R Examples, Third Edition, by Robert H. Shumway and David S. Stoffer, Springer Verlag.

*Time Series Analysis and Forecasting by Example, 1st Edition, by Søren Bisgaard and Murat Kulahci, John Wiley & Sons.

I hope it helps you. Best of luck!
A: There's the NBER Summer Institute "What's New in Time Series Econometrics" (not sure whether this material is gated or not). There are videos with accompanying slides. The lectures are given by a pair of professors (Stock and Watson) who are known for their popular undergraduate econometrics textbook.
A: Time Series Analysis: Univariate and Multivariate Methods by  William Wei and David P. Reilly - is a very good book on time series and quite inexepnsive. There is am updated version but at a much higher price. It does not include R examples. It explicitely includes a great discussion/presentation of Intervention Detection procedures which are ignored in simplified solutions/introductory textbooks.
A: Part Four of Damodar Gujarati and Dawn Porter's Basic Econometrics (5th ed) contains five chapters on time-series econometrics - a very popular book! It contains lots of exercises, regression outputs, interpretations, and best of all, you can download the data from the book's website and replicate the results for yourself. Another good book is Stock and Watson's Introduction to Econometrics.
Starting with Hamilton was admirable, but I'd say read through both of the time-series sections in the two books that I just mentioned and then move on to something like Walter Enders' Applied Econometric Time Series or Terrence C Mill's The Modelling of Financial Time Series.
After this (and probably after some review of mathematical economics) then you should be able to sit down and read Hamilton comfortably. 
Note: Box & Jenkins' 1970 classic Time series analysis: Forecasting and control is obviously more concentrated (i.e. narrower in content) than the "modern textbooks" that I mentioned, but I'd say that anyone who wants to get a real good understanding of time-series shouldn't leave this off their reading list. 
A: It depends on how much math you want.  For a less mathematically-intense treatment, Applied Econometric Time Series by Enders is well-regarded.
A: There are a few books that might be useful. If you are mathematically challenged you might want to start with two SAGE books by Mcdowall, Mcleary, Meidinger and Hay called "Interrupted Time Series Analysis" 1980  OR "Applied Time Series Analysis" by Richard McLeary.  As you learn more about time series and decide that you you want more than prose and that you are willing to suffer through some math the Wei text published by Addison-Wessley entitled "Time Series Analysis" would be an excellent choice. In terms of web-based educational material, I have written a lot of useful material which can be viewed at http://www.autobox.com/cms/index.php/afs-university/intro-to-forecasting entitled "Introduction to Forecasting".  
A: If you use Stata, Introduction to Time Series Using Stata by Sean Becketti is a solid gentle introduction, with many examples and an emphasis on intuition over theory. 
I think this book would complement Ender rather well.
The book opens with an intro to Stata language, followed by a quick review of regression and hypothesis testing.
The time series part starts with moving-average and Holt–Winters techniques to smooth and forecast the data. The next section focuses on using these for techniques forecasting. These methods are often neglected, but they work rather well for automated forecasting and are easy to explain. Becketti explains when they will work and when they won't.
The next chapters cover single-equation time-series models like autocorrelated disturbances, ARIMA, and ARCH/GARCH modeling.
In the end, Becketti discusses multiple-equation models, particularly VARs and VECs, and non-stationary time series.
A: HILL GRIFFITHS LIM 2011 "Principles of Econometrics" 4E Wiley
Advantages:
(1) Very easy to follow. Topics are well presented. Even though I did not take any econometric course in my life, I easily grasped introductory econometrics with the book.
(2) There are supplemantary books to understand HILL's book:
a. Using EViews for Principles of Econometrics
b. Using Excel for Principles of Econometrics
c. Using Gretl for Principles of Econometrics
d. Using Stata for Principles of Econometrics
Disadvantages:
(1) There is no "Using R for Principles of Econometrics"!
R is industry standard. R is better than Python. Maths in mind can be best reflected to code via R (I am saying this as a person who wrote VBA modules in Excel, wrote Gretl codes, wrote Eviews codes).
I self-started Econometrics with "GREENE 2011 Econometric Analysis - W.H. GREENE 7E PearsonPrentice Hall"
This is also nice, but more theoretical; may be difficult for starters.
In summary, I strongly recommend grasping Econometrics with Hill's book, and apply that understanding via another Econometry book that is based on R.
A: I haven't seen anybody mention the book by Gloria Gonzalez-Rivera "Forecasting for Economics and Business". I have found it to be the best kept secret in the time series space. It is a terrific book. It will give you more intuition than Diebold, more context than Enders, and will actually be readable unlike Hamilton. With much of the outstanding literature on time series, one may wonder if top time series experts are sworn to some sort of secrecy to not explain time series forecasting to others in an understandable way lest others join their little circle of trust. Gloria Gonzalez-Rivera's book let's you into this exclusive time series circle; it was a precious find for me. 
A: Lütkepohl "New Introduction to Multiple Time Series Analysis" (2005) is quite up to date and offers a clear exposition.
A: I think the word 'introductory' should be banned in statistics. Not many without a strong background in statistics will find topics such as vector autoregressive models or ARDL to be introductory nor the Hamilton work and many others mentioned. There is a a huge gap between academic and practitioner audiences in this topic I feel.  Having looked hard as a practitioner for time series books over the last 7 year, I have found few that are introductory and either fewer aimed at practitioners as compared to academics. The Chadwick book already mentioned was useful (practical). I found Anders Milhoj to be useful for exponential smoothing, but he uses SAS. Many issues that practitioners worry about, such as cleaning and finding data are simply not addressed in many works on time series nor the use of expert judgement to correct mistakes. Concepts such as using multiple models to triangulate results (found to correct error) never show up in the academic time series books I have encountered. I have found on line links better than books for this, although I plan to read many of the works suggested.
A: I will recommend you a textbook related with time series analysis. I read this book and got the idea. This book is very easy to understand.
The link for the book :https://a-little-book-of-r-for-time-series.readthedocs.io/en/latest/src/timeseries.html
This book is very good because it shows everything from scratch. This book shows.

*

*how to read time series data.


*plotting time series.


*Decomposing time series


*Decomposing non seasonal data


*Decomposing seasonal data


*seasonality adjusting


*forecasting using exponential smoothing
and many more topics which is very helpful and very clear. If you read this book you can get a good understanding about time series analysis.
