Course Content

Time Series Analysis

1. Time Series: Let's Start

Introduction Practical Examples Model Types Implementation in Python Visualization

2. Time Series Processing

Reading and Visualizing Data Stationarity Autocorrelation Decomposition

3. Time Series Visualization

Simple Time Series Customize Your Plot Challenge

4. Stationary Models

Examples of Stationary Time Series Simple Moving Average Autoregression Autoregressive-Integrated-Moving-Average Model Challenge

5. Non-Stationary Models

Examples of Non-Stationary Time Series Convert Non-Stationary Data to Stationary Challenge 1 Challenge 2

6. Solve Real Problems

Weather Forecasting Store Demand Forecast Financial Markets Predict the Emergence of Repeat Customers Medicine: EEG Forecasting

Autoregression

Let's move on to the review of the autoregressive model:

The formula is similar to the linear regression formula, which is where the name comes from. Instead of the coefficient - the past value of x is used.

With statsmodels we can run an autoregressive model AutoReg():

If you notice, the predictions made by the autoregressive model are more accurate than those of the simple moving average.

Let's learn how to evaluate the received results of the trained models. The error is calculated using the mean-squared error. This is done simply with the help of functions sqrt() and mean_squared_error():

In the same way, we calculate the error value for the previous model:

The smaller the MSE value, the correspondingly smaller the error.

Task

Create an autoregressive model and train it on the dataset shampoo.csv.

Create an autoregression model (Autoreg) with 6 lags for the "Sales" column of the df DataFrame.
Fit the model to data.
Make predictions using the model. Start forecasting at the first row (the start parameter), and set the dynamic parameter to False.
Visualize the results: show the first 150 observations of the "Sales" column of the df DataFrame within the first call of the .plot() function and the first 150 predicted values within the second call.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 4. Chapter 3

Autoregression

Let's move on to the review of the autoregressive model:

The formula is similar to the linear regression formula, which is where the name comes from. Instead of the coefficient - the past value of x is used.

With statsmodels we can run an autoregressive model AutoReg():

If you notice, the predictions made by the autoregressive model are more accurate than those of the simple moving average.

In the same way, we calculate the error value for the previous model:

The smaller the MSE value, the correspondingly smaller the error.

Task

Create an autoregressive model and train it on the dataset shampoo.csv.

Create an autoregression model (Autoreg) with 6 lags for the "Sales" column of the df DataFrame.
Fit the model to data.
Make predictions using the model. Start forecasting at the first row (the start parameter), and set the dynamic parameter to False.
Visualize the results: show the first 150 observations of the "Sales" column of the df DataFrame within the first call of the .plot() function and the first 150 predicted values within the second call.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 4. Chapter 3

Autoregression

Let's move on to the review of the autoregressive model:

The formula is similar to the linear regression formula, which is where the name comes from. Instead of the coefficient - the past value of x is used.

With statsmodels we can run an autoregressive model AutoReg():

If you notice, the predictions made by the autoregressive model are more accurate than those of the simple moving average.

In the same way, we calculate the error value for the previous model:

The smaller the MSE value, the correspondingly smaller the error.

Task

Create an autoregressive model and train it on the dataset shampoo.csv.

Create an autoregression model (Autoreg) with 6 lags for the "Sales" column of the df DataFrame.
Fit the model to data.
Make predictions using the model. Start forecasting at the first row (the start parameter), and set the dynamic parameter to False.
Visualize the results: show the first 150 observations of the "Sales" column of the df DataFrame within the first call of the .plot() function and the first 150 predicted values within the second call.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Let's move on to the review of the autoregressive model:

The formula is similar to the linear regression formula, which is where the name comes from. Instead of the coefficient - the past value of x is used.

With statsmodels we can run an autoregressive model AutoReg():

If you notice, the predictions made by the autoregressive model are more accurate than those of the simple moving average.

In the same way, we calculate the error value for the previous model:

The smaller the MSE value, the correspondingly smaller the error.

Task

Create an autoregressive model and train it on the dataset shampoo.csv.

Create an autoregression model (Autoreg) with 6 lags for the "Sales" column of the df DataFrame.
Fit the model to data.
Make predictions using the model. Start forecasting at the first row (the start parameter), and set the dynamic parameter to False.
Visualize the results: show the first 150 observations of the "Sales" column of the df DataFrame within the first call of the .plot() function and the first 150 predicted values within the second call.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 4. Chapter 3

Switch to desktop for real-world practiceContinue from where you are using one of the options below