My professor recommended this book.
I read Machine learning for Asset Managers and Practical Time Series Analysis yesterday.
Both were good in their own way. The former is much more advanced and presents some ideas that you might not have been exposed to and the latter is much more intermediate but has hands-on examples, though the examples are a little simplistic and often with mediocre results.
It is quite good and covers a lot but it uses R and python interchangeably. I prefer python though.
I found this coursera course and going to study it. This course uses R as well. Guess I need to learn and use R then. But I am planning to make a python version of notes for this course.
*TS models and methods are poorly introduced in this book.
Mainly reference: "Practical Time Series Analysis" (2017) and its github repo.
Chapter 1: Introduction to Time Series
- Different types of data
- Cross-sectional data
- Time series data
- Panel data
- Internal structures of time series
- General trend
- Seasonality
- Run sequence plot
- Seasonal sub series plot
- Multiple box plots
- Cyclical changes
- Unexpected variations
- Models for time series analysis
- Zero mean models
- Random walk
- Trend models
- Seasonality models
- Autocorrelation and Partial autocorrelation
-
Advanced processing and visualization of time series data
-
Resampling time series data
- Group wise aggregation
- Moving statistics
- Stationary processes
- Differencing
- First-order differencing
- Second-order differencing
- Seasonal differencing
- Augmented Dickey-Fuller test
- Time series decomposition
- Moving averages
- Moving averages and their smoothing effect
- Seasonal adjustment using moving average
- Weighted moving average
- Time series decomposition using moving averages
- Time series decomposition using
statsmodels.tsa