## 8 – M2L4 11 Outro V1

In this lesson, we’ve seen statistical methods such as regression, autoregression, and moving averages. We’ve also seen Kalman filters, particle filters, and recurrent neural networks. Congratulations on making it through this lesson

## 7 – M2L4 09 Recurrent Neural Networks V5

The last model we’ll introduce here are recurrent neural networks, which are used in natural language processing as well as time series. Recurrent neural networks are as the name implies neural networks. Neural networks are models that can be thought of as many regressions stacked in series and in parallel. When I say the regressions … Read more

## 6 – M2L4 08 Particle Filter V4

Next, we’ll discuss the particle filter, a type of genetic algorithm that is also used for self-driving cars as well as time series. By genetic algorithm, I mean that we apply natural selection to improve our estimates. This will become more clear in a bit. Let’s start with a thought experiment. Imagine that we can … Read more

## 5 – M2L4 07 Kalman Filter V4

We’ll now discuss the Kalman filter, which is used for time series in self-driving cars and even flying cars. But first, let’s look back at regression and autoregressive moving averages to see how Kalman filters are different. Recall that with autoregressive moving averages, we must choose the lag number for autoregression and also the lag … Read more

## 4 – M2L4 05 Advanced Time Series Models V5

Autoregressive and moving average models tend to capture different relationships. The nice thing is, you can get the best of both by adding them together. An autoregressive moving average is defined with a p and q. The p is the lag for the autoregression, the q is the lag for moving average. A variation of … Read more

## 3 – M2L4 03 Moving Average Models V5

Another way to model time series is to think of the stock return hovering around in moving average. As an analogy, imagine that you’re walking at night while holding a lantern, a moth flies around the lantern, moving a bit randomly, but still following the general path of your lantern. In this analogy, the lantern … Read more

## 2 – M2L4 02 Autoregressive Models V5

When we look at log returns of a stock, we assume that the previous period’s value gives us some insight into the next period’s value. It’s also reasonable to assume that the past couple of data points give us hints as to what the next value will be. This is the main principle behind Auto-regressive … Read more

## 1 – M2L4 01 Time Series Modeling V4

Welcome to the lesson on time series analysis. Time series are data that are collected at regular intervals. We will cover two statistical methods, autoregression and moving averages. This will give us the foundation to cover two more advanced methods, autoregressive moving averages and autoregressive integrated moving averages. From there, we will cover two machine-learning … Read more