Time Series CheatSheet - v 9.0

 This time we will have few more updates to the Time Series cheat sheet that can be seen from the following image. Image size was changed as we are covering few more components and you can get to the original file from Time-Series-Cheat-Sheet

Improvements in v 9.0.

1. Bench-Mark dataset

Few research papers have indicated that there are benchmark datasets for Time Series analysis so those are included. 

2. Timestamp Attribute Derivation

During the analysis of datasets, it was found that there are some datasets that do not have an explicit timestamp attribute. In some datasets, the time attribute is distributed between multiple columns such as year, month, day, hour, minute etc. In addition, sometimes there are no timestamp attributes and that has to be generated. 

3. Time Series Reconstruction

By looking at few more research papers, we identified that there are many different techniques of Time Series Reconstruction. 

Time Series Cheatsheet 6.0

We have been releasing the Time Series Cheatsheet during this year in order to capture all the features of Time Series Forecasting. This is the newest release of that effort. 

You can access this list from this link. 

The following are the major changes done from the previous versions. 

1. Separation of Forecasting algorithms into main four categories. 

            a) Time Series Non-Linear Methods

            b) Classical Statistical Time Series Linear Methods

            c) Supervised Machine Learning Methods

            d) Regression Methods

2. Inclusion of Stationary Validation Techniques

Time Series Cheat Sheet v 5.1.0.0

Minor changes were done after the last release. 

 

Time Series Cheat Sheet - v 5.0.0.0

This is the latest update on the Time Sheet Cheatsheet. This is after analysing the Time Series Features in Orange Data Mining Tool. 

In the new release, the following are new including. 

1. Time series Specific diagrams are included so that they can display the existing properties of the Time Series for easy understanding. Spilarogram, Periodogram and Correlogram are those diagrams. 
2. 1st and 2nd Order differencing are included for data normalization. 
3. Interpolation is included as missing value replacement. 
4. Granger Causality is included as a Time Series technique.

Next, we will be evaluating Rapid Minner for the Time Series Forecasting and hopefully new version of the Time Series Cheatsheet will be released at end of the next month. 

Cheat Sheet - Time Series Forecasting in Orange

A cheat sheet or crib sheet is a concise set of notes used for quick reference. We are developing cheat sheets for several aspects such as Time Series Forecasting, Recommender Systems, etc during the last few months. In Time Series, we have gone into developing cheatsheets for products such as Microsoft SQL Server, Azure Machine Learning, and Weka. 

After discussing the Time Series features in Orange, now let us see the cheat sheet for the Orange tool. 

We found a few features that were not found in the previous tools that we discussed before. Importantly, in Orange, there are three diagrams that show the properties time series. We will discuss those diagrams in a separate post in detail.

In Orange, Interpolate technique is used to find and replace the missing data. In the Time Series Forecasting, ARMA, ARIMA, ARIMAX and VAR are possible techniques that can be used in Orange. 

We will be meeting Rapid Miner as the next tool in our journey. Stay tuned!

Time Series Cheat Sheet - v 4.5.0.1

After analysing features in the WEKA data mining tool, few features were added to the Time Series Cheat Sheet. 

Apart from the graphical cleanup, a couple of features are added to the cheat sheet. Though we have identified missing values before, there can be a situation where data is not available on some dates. For example, stock data may not be available on holidays.  

Different types of regressions are included in Statistical techniques as there are instances where regression is performing efficiently than other features. 

Cheat Sheet for Recommender Systems

Recommender systems have become an important system in today's competitive world. Mainly you can utilize these types of systems to improve sales by target specific customer groups. In order to identify all the options in the Recommender system, the follow cheat system was developed.

 

Time Series Cheat Sheet v4.0.0.0

This is the exercise of identifying the features of Time Series to facilitate the research of Design and Implementation a framework for Time Series Modelling using Multi-Agent Technologies. In the early stage of the research, we have identified many features for Time Series as shown below. 

In this version, we have included identification of Holidays and Public data sets such as Rainfall, Temperature etc. Data Normalization techniques and Outlier detection is also identified. 

Out of the existing techniques, we have extended the evaluation parameters and advanced techniques such as wavenets, and Graph Neural Networks. Further, to facilitate the operations, we have included the exit condition for the modelling and this enables us to detect the non-ending modelling. In addition, we have added the blocked techniques pre-configuration where users can explicitly define the techniques that should be modelled. 

In this research, we are looking into different tools to identify the features of the Time series. Until now we have analysed SQL Server and Azure services. Next month, we will be analysing Weka and Orange tools.