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This information can be useful to find documents similarities based on their thematic. You can also list all of your topic models. Specifies a list of terms to ignore when performing term analysis. This can be used to change the names of the fields in the topic model with respect to the original names in the dataset or to tell BigML that certain fields should be preferred. All text fields in the dataset Specifies the fields to be considered to create the topic model.

If multiple fields are given, the text field values for each row will be concatenated so that each row is still considered to be one document.

If it is unset, it will be chosen automatically based on the number documents (i. The minimum value is 2 and maximum value is 64. Example: "MySample" tags optional Array of Strings A list of strings that help classify and index your topic model. Computation is linear with respect to this parameter. The minimum value is 128 and maximum value is 16384. The minimum value is 1 and maximum value is 128.

Example: true You can also use curl to customize a new topic model. Once a topic model has been successfully created it will have the following properties.

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Topic Model Status Creating a topic model is a process that can take just a few seconds or a few days depending on the size of the dataset used as input and on the workload of BigML's systems. The topic model goes through a number of states until its fully completed. Through the status field in the topic model you can determine when the topic model has been fully processed and ready to be used to create predictions. Thus when retrieving a topicmodel, it's possible to specify that only a subset of fields be retrieved, by using any combination of the following parameters in the query string (unrecognized parameters are ignored): Fields Filter Parameters Parameter TypeDescription fields optional Comma-separated list A comma-separated list of field IDs to retrieve.

To update a topic model, you need to PUT an object containing the fields that you want to update to the topic model' s base URL. Once you delete a topic model, it is permanently deleted. If you try to delete a topic model a second time, or a topic model that does not exist, you will receive a "404 not found" response. However, if you try to delete a topic model that is being used at the moment, then BigML.

To list all the topic models, you can use the topicmodel base URL.

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By default, only the 20 most recent topic models will be returned. You can get your list of topic models directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your topic models. Time Series Last Updated: Friday, 2017-10-27 12:23 A time series model is a supervised learning method to forecast the future values of a field based on its previously observed values.

It is used to analyze time based data when historical patterns can explain the future behavior such as stock prices, sales forecasting, website traffic, production and inventory analysis, weather forecasting, etc.

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A time series model needs to be trained with time series data, i. BigML implements exponential smoothing to train time series models. Time series data is modeled as a level component and it can optionally include a trend (damped or not damped) and a seasonality components as explained below:Forecast equation Level equation Forecast equation Level equation Trend equation Forecast equation Level equation Damped trend equation Forecast equation Level equation Trend equation Seasonality equation The different components can have variations, e.

As a result of combining the different variations for each component, several models can be trained for a given objective field.Simply by creating a 300-word article and building 500 unnatural links to it from (even very low-quality blogs) with unique anchor text, spread out over a few months, you could achieve top rankings.

It can still be effective, but this activity comes with an increased risk of Google penalty, even when you are an experienced black-hat.

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Google has a few surprises for Webmasters bending the rules using what they call spammy links. My blog is not a black hat SEO blog. I work with companies who are thinking of the long-term health of their business and who understands that the key to ranking in Google in the future is by making the website better, richer in content and the best user experience it can be. Too many people submit spam reports and too many people engage in negative SEO for me to invest too much in unnatural links these days.

If you want to rank in Google with any confidence in the long-term, you are going to have to INVEST in GOOD, IN-DEPTH content, and a few GREAT links. You can ALWAYS pick up the odd decent link to good content. This page is an example. I deleted some old posts on the Hobo blog, reworked some out of date advice, merged similar content. I added some videos from Google, who now advise on the subject-matter where they once did not, and I created this in-depth page that, I hope, is of some use for beginners wishing to market a website successfully in Google in 2017.

Some bloggers did notice, however. But before that, let us look at the big traders or the warren buffets of this world. Interestingly, you can easily see which sites you are in competition with in the SERPs by looking for similar ranking keywords. Take for example Hobo-Web. Quality content drives the organic link building end-results Google claims it wants to reward with visibility in its SERPs.

High-quality seo copywriting has never been so important. Google often still relies on links to find, index, categorise and rate websites (and pages) in 2017. Content will always be king (especially in the future) but without links, content can still be a moot point.

Links are like lasers. Links have the potential to make a site hot, and send that site to the top of the results.

Some links get hotter in time, some get colder, and some disappear as each site is affected by those linking to it and from it. In the diagram below, you can see in any collection of pages, there is natural heat, naturally hot and cold sites, because of the natural interlinking going on between pages.If it is omitted, then the missing values are filled via spline interpolation before fitting.

Example: "median" description optional A description of the time series up to 8192 characters long.

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Example: "This is a description of my new time series" error optional Any of the following values to specify types of ETS models: 1 (additive), 2 (multiplicative). Multiplicative error models are only available when the objective field has strictly positive values (greater than 0).

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Example: 100 name optional The name you want to give to the new time series. Example: 2 tags optional A list of strings that help classify and index your time series. This will be 201 upon successful creation of the time series and 200 afterwards.

Make sure that you check the code that comes with the status attribute to make sure that the time series creation has been completed without errors.

This is the date and time in which the time series was created with microsecond precision. True when the time series has been built in development mode. See the Forecast Result Object definition below.

In a future version, you will be able to share time series with other co-workers or, if desired, make them publicly available. This is the date and time in which the time series was updated with microsecond precision. A map with keys (objective field identifiers) and with values (dataset identifiers). Those datasets contain a timestamp column, a copy the original data of the objective field, and one column per model with the values that that particular submodel computes for the objective time series.

ETS error type parameter: 1 (additive), 2 (multiplicative) The results of the ETS fits. A dictionary with an entry per field in your data. Each entry is a list of maps. Integerinterval: The interval between each forecast value. ETS trend type parameter: 0 (none), 1 (additive), 2 (multiplicative) The Akaike Information Criterion score. The Small-sample corrected AIC score. Only included for ets models where trend is not none.

The Bayesian Information Criterion score. The final fitted state for the ETS model with the following entries: b - Trend state. Floatl - Level state. Float s - Seasonal state. Only for ets models with seasonality. Only included for ets models where seasonality is not none. The initial fitted state for the ETS model with the following entries: b - Trend state.


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