Transformers in Time Series: A Survey : https://arxiv.org/abs/2202.07125

https://github.com/qingsongedu/time-series-transformers-review

Ashish Patel Thank you, very interesting!

Hello Ella, Sure, I can help you with that.
There are a few ways to gather time series data from a website you cannot access.
One way is to use a web scraping tool. Several web scraping tools are available, both free and paid. Some popular web scraping tools include:
===============================================================================
> - Beautiful Soup: This is a free and open-source Python library for web scraping.
> - Selenium: This is a free and open-source tool for automating web browsers.
> - WebHarvy: This is a paid web scraping tool that offers a variety of features, including support for multiple browsers and languages.
Once you have chosen a web scraping tool, you will need to configure it to scrape the data from the website that you are interested in. This may involve specifying the URL of the website, the elements that you want to scrape, and the format in which you want the data to be saved.
Once the web scraping tool is configured, you can run it to scrape the data from the website. The data will be saved in a file, which you can then import into a time series analysis tool.
Another way to gather time series data from a website you cannot access is to use a data API. A data API is a web service that provides access to data.
There are a number of data APIs available, both free and paid. Some popular data APIs include:
===============================================================================
> - Quandl: This is a free and open-source data API that provides access to financial data.
> - Google Finance: This is a free data API that provides access to financial data.
> - Yahoo Finance: This is a free data API that provides access to financial data.
Once you have found a data API that provides access to the data you are interested in, you must sign up for an account and obtain an API key. Once you have an API key, you can use it to make requests to the data API. The data will be returned in a JSON format, which you can then import into a time series analysis tool.
If you do not have any historical data to work with, you can start collecting data now and use it to create a time series. However, it is important to note that the accuracy of your predictions will depend on the amount of data that you have. The more data you have, the more accurate your predictions will be.
Here are some additional tips for gathering time series data:
===============================================================================
> - Use a variety of sources: It is important to use a variety of sources when gathering time series data. This will help to ensure that you have a complete and accurate dataset.
> - Check for errors: It is important to check for errors in your data. This can be done by using a data validation tool.
> - Clean your data: It is important to clean your data before you use it for analysis. This can be done by removing any outliers or missing values.
> - Document your data: It is important to document your data. This will help you to understand the data and to make it easier to share with others.
Timeseries Dataset GitHub : https://github.com/WenjieDu/TSDB
I hope this helps!
Regards,
Ashish Patel

Thank you for the resource, and the comprehensive response. Much appreciated!

This is one of the Nice Book Alexey Grigorev

Hello Harshit Lamba! You are correct that data alone is not sufficient for predicting black swan events. As you mentioned, there are many external factors that can contribute to these events, and these factors are often difficult to quantify or measure. As a result, it is very difficult to develop models that can reliably predict black swan events.
That said, there are a number of research papers that have been published on the topic of black swan event prediction. Some of these papers have proposed methods for using machine learning techniques to identify potential black swan events. Other papers have focused on developing methods for mitigating the impact of black swan events.
One example of a research paper that has proposed a machine learning-based method for black swan event prediction is the paper “Black Swan Event Prediction with Machine Learning” by Ethan Pickering and colleagues. In this paper, the authors propose a method for using a Bayesian neural network to predict the occurrence of black swan events. The authors evaluated their method on a dataset of historical data, and they found that their method was able to predict black swan events with a high degree of accuracy.
Another example of a research paper that has focused on mitigating the impact of black swan events is the paper “Black Swan Event Mitigation with Portfolio Diversification” by John Cochrane. In this paper, the author argues that portfolio diversification can help to mitigate the impact of black swan events. The author shows that by diversifying a portfolio across a wide range of assets, investors can reduce their exposure to the risk of any one asset experiencing a black swan event.
The research on black swan event prediction is still in its early stages, and no single method has been shown to be universally effective. However, the research that has been conducted to date suggests that machine learning techniques and portfolio diversification can be used to help mitigate the impact of black swan events.
The book “Hands-on Time Series Analysis with Python” by Ashish Patel, does not specifically address the topic of black swan event prediction. However, the book does cover a number of topics that are relevant to black swans’ event prediction, such as forecasting, anomaly detection, and risk management.
Overall, the research on black swan event prediction is still in its early stages, but there is a growing body of research that suggests that machine learning techniques and portfolio diversification can be used to help mitigate the impact of black swan events.

Hi Daniel,
Thank you for your questions. I am happy to answer them.

• Uncertainty: There are a number of approaches that can be used to express uncertainty in time series forecasts. Some of the most common methods include:
  ◦ Confidence intervals: Confidence intervals provide a range of values that are likely to contain the true value of the forecast. The width of the confidence interval indicates the level of uncertainty in the forecast.
  ◦ Prediction intervals: Prediction intervals provide a range of values that are likely to include the true value of the forecast, even if there are unexpected changes in the underlying data. The width of the prediction interval is typically wider than the confidence interval, as it takes into account the possibility of unexpected changes.
  ◦ Conformal prediction: Conformal prediction is a non-parametric approach to uncertainty estimation that does not make any assumptions about the underlying distribution of the data. This makes it a particularly useful method for forecasting data that is not normally distributed.
  In my experience, the most useful approach to expressing uncertainty in time series forecasts depends on the specific application. For example, if the forecast is being used to make important decisions, then it is important to use a method that provides a narrow prediction interval.
  However, if the forecast is being used for less critical purposes, then a wider confidence interval may be sufficient.
  
• Explainability: Explainability is another important consideration when developing time series forecasts. Explainability refers to the ability to understand how a model makes its predictions. This can be important for a number of reasons, such as:
  ◦ Trust: Users are more likely to trust a model if they can understand how it works.
  ◦ Interpretability: In some cases, it may be necessary to interpret the model’s predictions in order to make decisions.
  ◦ Debugging: If the model is not performing well, it can be helpful to understand why in order to improve it.
  There are a number of ways to improve the explainability of time series models. Some of the most common methods include:
  > - Feature selection: Feature selection can be used to identify the most important features for the model. This can help to reduce the complexity of the model and make it easier to understand.
  > - Interpretation tools: There are a number of tools that can be used to interpret the predictions of a time series model. These tools can be used to visualize the model’s predictions and to understand the relationships between the features and the predictions.
  > - Communication: It is important to communicate the results of the model to the users in a clear and understandable way. This can be done through reports, presentations, or other means.
  Note: Lime will help in this case to ExplainableAI of Time Series
  
• Classical vs. advanced methods: The debate over the usefulness of classical vs. advanced methods for time series forecasting is likely to continue for some time. There is no clear consensus on which approach is better, as the best approach depends on the specific application.
  In my experience, classical methods such as ARIMA and LightGBM can be very effective for time series forecasting. However, advanced methods such as Transformers can also be effective, especially for forecasting complex data sets.
  The choice of which approach to use should be made on a case-by-case basis, taking into account the specific requirements of the application.
  I hope this answers your questions. Please let me know if you have any other questions.
  Best regards,
  Ashish

Hello raki,
Thank you for your questions. I am happy to answer them.
MLOps principles can be applied to the deployment of time series models in a production environment by:
================================================================================
> - Automating the entire ML lifecycle: This includes data collection, preprocessing, feature engineering, model training, evaluation, and deployment.
> - Ensuring reproducibility: This means that the same results can be obtained from the same data and code, which can be difficult to achieve with black-box models like Transformers.
> - Monitoring the model performance: This includes tracking the accuracy of the model’s predictions, as well as any changes in the underlying data that could impact the model’s performance.
> - Retraining the model as needed: This is important because the underlying data may change over time, which can impact the model’s accuracy. The frequency of retraining depends on the specific application. For example, a model that is used to forecast daily sales may need to be retrained more often than a model that is used to forecast quarterly earnings.
Some of the specific challenges or considerations when it comes to deploying and maintaining time series models in a production environment include:
================================================================================
> - Data quality: The quality of the data used to train the model is critical to the accuracy of the model’s predictions.
> - Model complexity: Complex models can be difficult to understand and interpret, which can make it difficult to debug and improve the model.
> - Model bias: Models can be biased, which can lead to inaccurate predictions.
> - Model drift: The underlying data may change over time, which can impact the model’s accuracy.
By following MLOps principles, organizations can improve the accuracy, reliability, and maintainability of their time series models.
Here are some additional tips for deploying and maintaining time series models in a production environment:
================================================================================
> - Use a cloud-based platform: This can make it easier to manage and scale the model, as well as to access and store the data.
> - Use a continuous integration/continuous delivery (CI/CD) pipeline: This can automate the process of deploying the model to production, which can help to ensure that the model is always up-to-date.
> - Use a monitoring system: This can track the performance of the model and alert you if there are any problems.
> - Have a plan for retraining the model: This will help to ensure that the model remains accurate over time.

Hi Michael Enudi,
Thank you for your kind words.
There are a few reasons why many Python books on time series don’t pay as much attention to volatility clustering as R books.
> - R has a number of packages that are specifically designed for volatility clustering analysis, such as the rugarch package. Python does not have a dedicated package for volatility clustering analysis, so it is often covered in more general time series books.
> - Volatility clustering is a more advanced topic in time series analysis, and it is not essential for understanding the basics of time series forecasting. Many Python books are aimed at beginners, so they may not cover volatility clustering in detail.
> - Volatility clustering is a relatively new topic in time series analysis, and it is still being researched. As a result, there is not as much literature on volatility clustering in Python as there is in R.
There are a number of other tools besides classical methods of ARCH and GARCH family of tools to analyze variability in volatility and how to predict such. Some of these tools include:
> - Power GARCH: This is a generalization of the GARCH model that allows for more flexibility in the modeling of volatility.
> - Exponential GARCH: This is a variant of the GARCH model that uses an exponential decay function to model the autocorrelation of the squared errors.
> - TARCH: This is a threshold autoregressive conditional heteroskedasticity model that allows for different levels of volatility depending on the value of the underlying time series.
> - VARX: This is a vector autoregressive model that allows for exogenous variables to impact the volatility of the time series.
The choice of which tool to use depends on the specific application. For example, if the goal is to forecast volatility for a financial asset, then a model such as the GARCH or Power GARCH model may be appropriate. However, if the goal is to forecast volatility for a non-financial asset, then a model such as the TARCH or VARX model may be more appropriate.
It is important to note that no single tool is perfect for all volatility forecasting applications. As a result, it is often necessary to use a combination of tools to get the best results.
Best Regards,
Ashish Patel