Financial Time Series Data- Welcome to this course on financial time series analysis using R. In this course, we will learn abou...

## COURSE

## Financial Time Series Analysis in R

- Let's look at a few commands that we will frequently use while exploring time series data. ### leng...
- While we can explore time series data using commands such as `print()`, `head()`, `tail()`, etc in R...
- In the examples we saw earlier, we had good quality data with all values available for all time inde...
- In R, we can use the `ts()` function to create a time series object. ### Usage Below is a simplifi...
- In R, objects can be of different class such as vector, list, dataframe, ts, etc. When you load a da...
- Let's take one more example of plotting financial time series data. This time we will use the `EuSto...
- Time series have several characteristics that make their analysis different from other types of data...
- A common assumption made in time series analysis is that one of the components of the pattern exhibi...
- Most financial and economic times series are not stationary. Even when you adjust them for seasonal ...
- We will now learn about how we can perform the mathematical transformations in R in order to make a ...
- ### Removing Variability Using Logarithmic Transformation Since the data shows changing variance ov...
- Autocorrelation is an important part of time series analysis. It helps us understand how each observ...
- By now we have a strong foundational understanding of various concepts essential for time series ana...
- If we combine differencing with autoregression and a moving average model, we obtain a non-seasonal ...
- The function `arima.sim()` can be used to simulate data from a variety of [time series models](https...
- When a series follows a random walk model, it is said to be non-stationary. We can stationarize it b...
- AutoRegressive (AR) model is one of the most popular time series model. In this model, each value is...
- We will now see how we can fit an [AR model](https://financetrain.com/autoregressive-ar-model-in-r/)...
- Now that we know how to estimate the AR model using ARIMA, we can create a simple forecast based on ...
- A Moving Average is a process where each value is a function of the noise in the past observations. ...
- We will now see how we can fit an MA model to a given time series using the `arima()` function in R....
- We now have a fair idea about how we can use [ARIMA modelling](https://financetrain.com/arima-modeli...
- The first step is to identify a possible model for a given time series. To do so, we need three thin...
- In this lesson, we will take a new dataset (stock prices) and use all that we have learned to create...
- ### auto.arima() Function R also has a package called forecast, which contains many forecasting f...
- This course provided an overview of the fundamentals of time series analysis and how we can perform ...

## LESSONS

Exploring Time Series Data in R

Plotting Time Series in R

Handling Missing Values in Time Series

Creating a Time Series Object in R

Check if an object is a time series object in R

Plotting Financial Time Series Data (Multiple Colu...

Characteristics of Time Series

Stationary Process in Time Series

Transforming a Series to Stationary

Time Series Transformation in R

Differencing and Log Transformation

Autocorrelation in R

Time Series Models

ARIMA Modeling

Simulate White Noise (WN) in R

Simulate Random Walk (RW) in R

AutoRegressive (AR) Model in R

Estimating AutoRegressive (AR) Model in R

Forecasting with AutoRegressive (AR) Model in R

Moving Average (MA) Model in R

Estimating Moving Average (MA) Model in R

ARIMA Modelling in R

ARIMA Modelling - Identify Model for a Time Series

Forecasting with ARIMA Modeling in R - Case Study

Automatic Identification of Model Using auto.arima...

Financial Time Series in R - Course Conclusion