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SequencePredict [{seq1,seq2,…}]

generates a SequencePredictorFunction […] based on the sequences given.

SequencePredict [training,seq]

attempts to predict the next element in the sequence seq from the training sequences given.

SequencePredict [training,{seq1,seq2,…}]

gives predictions for each of the sequences seqi.

SequencePredict ["name",seq]

uses the built-in sequence predictor represented by "name".

SequencePredict […,seq,prop]

give the specified property of the prediction associated with seq.

Details and Options
Details and Options Details and Options
Examples  
Basic Examples  
Scope  
Custom Sequence Predictors  
Built-in Sequence Predictors  
Options  
FeatureExtractor  
PerformanceGoal  
Method  
Possible Issues  
See Also
Related Guides
History
Cite this Page

SequencePredict [{seq1,seq2,…}]

generates a SequencePredictorFunction […] based on the sequences given.

SequencePredict [training,seq]

attempts to predict the next element in the sequence seq from the training sequences given.

SequencePredict [training,{seq1,seq2,…}]

gives predictions for each of the sequences seqi.

SequencePredict ["name",seq]

uses the built-in sequence predictor represented by "name".

SequencePredict […,seq,prop]

give the specified property of the prediction associated with seq.

Details and Options

  • The sequences seqi can be lists of either tokens or strings.
  • Sequences seqi are assumed to be unordered subsequences of an underlying infinite sequence.
  • In SequencePredict […,seq,prop], properties are as given in SequencePredictorFunction […]; they include:
  • "NextElement" most likely next element
    "NextElement"n individually most likely next n elements
    "NextSequence"n most likely next length-n sequence of elements
    "RandomNextElement" random sample from the next-element distribution
    "RandomNextElement"n random sample from the next-sequence distribution
    "Probabilities" association of probabilities for all possible next elements
    "SequenceProbability" probability for the predictor to generate the given sequence
    "SequenceLogProbability" log probability for the predictor to generate the sequence
    "Properties" list of all properties available
  • Examples of built-in sequence predictors include:
  • "Chinese" character-based Chinese-language text
    "English" character-based English-language text
    "French" character-based French-language text
    "German" character-based German-language text
    "Portuguese" character-based Portuguese-language text
    "Russian" character-based Russian-language text
    "Spanish" character-based Spanish-language text
  • The following options can be given:
  • FeatureExtractor Automatic how to preprocess sequences
    Method Automatic which prediction algorithm to use
    PerformanceGoal Automatic aspects of performance to try to optimize
    RandomSeeding 1234 what seeding of pseudorandom generators should be done internally
  • Typical settings for FeatureExtractor for strings include:
  • "SegmentedCharacters" string interpreted as a sequence of characters (default)
    "SegmentedWords" string interpreted as a sequence of words
  • Possible settings for PerformanceGoal include:
  • "Memory" minimize storage requirements of the predictor
    "Quality" maximize accuracy of the predictor
    "Speed" maximize speed of the predictor
    "TrainingSpeed" minimize time spent producing the predictor
    Automatic automatic tradeoff among speed, accuracy and memory
  • PerformanceGoal {goal1,goal2,…} will automatically combine goal1, goal2, etc.
  • Possible settings for RandomSeeding include:
  • Automatic automatically reseed every time the function is called
    Inherited use externally seeded random numbers
    seed use an explicit integer or strings as a seed
  • Possible settings for Method include:
  • "Markov" Markov model
  • In SequencePredict […,Method {"Markov","Order"order}], order corresponds to Markov process memory size.
  • In SequencePredict […,"SequenceProbability"], some probability mass is kept for unknown elements.
  • In SequencePredict [training,{},prop], {} is interpreted as an empty list of sequences rather than an empty sequence.

Examples

open all close all

Basic Examples  (1)

Train a sequence predictor on a set of sequences:

Wolfram Language code: sp = SequencePredict[{{[image], [image]}, {[image], [image], [image]}, {[image], [image], [image]}, {[image], [image]}}]

Predict the next element of a new sequence:

Wolfram Language code: sp[{[image], [image]}]

Obtain the probabilities of the next element given the sequence:

Wolfram Language code: sp[{[image], [image]}, "Probabilities"]

Obtain a random next element according to the preceding distribution:

Wolfram Language code: sp[{[image], [image]}, "RandomNextElement"]

Obtain multiple predictions at a time:

Wolfram Language code: sp[{{[image], [image]}, {[image], [image]}}]

Predict the most likely next element and reuse this intermediate guess to predict the following element:

Wolfram Language code: sp[{[image], [image]}, "NextElement" -> 2]

Predict the most likely following sequence:

Wolfram Language code: sp[{[image], [image]}, "NextSequence" -> 2]

Compare the probabilities for the preceding sequences:

Wolfram Language code: sp[{{[image], [image], [image], [image]}, {[image], [image], [image], [image]}}, "SequenceProbability"]

Scope  (4)

Custom Sequence Predictors  (3)

Train a sequence predictor on a list of strings:

Wolfram Language code: sp = SequencePredict[{"the cat is grey", "my cat is fast", "this dog is scary", "the big dog", "what a lovely cat", "this is not a dog"}]

Predict the next character following a given string:

Wolfram Language code: sp["the ca"]

Predict the next four characters:

Wolfram Language code: sp["the ca", "NextElement" -> 4]

Obtain the probabilities for each character to follow the given string:

Wolfram Language code: sp["the ca", "Probabilities"]

Train a sequence predictor on the list of common English words, each word treated as a sequence of characters:

Wolfram Language code: sp = SequencePredict[WordList[]]

Predict the most likely next character from a given sequence:

Wolfram Language code: sp["ab"]

In the previous example, each word is considered as a subsequence of an infinite sequence. Use the character | to mark boundaries between words:

Wolfram Language code: markedWords = "|" <> # <> "|"& /@ WordList[];

Build a new sequence predictor aware of word boundaries:

Wolfram Language code: sp = SequencePredict[markedWords]

Generate the beginning of an English-like word:

Wolfram Language code: sp["|", "RandomNextElement" -> 4]

Load a book from ExampleData :

Wolfram Language code: dq = ExampleData[{"Text", "DonQuixoteIEnglish"}];

Train a sequence predictor on this book:

Wolfram Language code: spchar = SequencePredict[{dq}]

Sample a random string in the book style:

Wolfram Language code: spchar[{"thou "}, "RandomNextElement" -> 20]

Train another sequence predictor, interpreting strings as word sequences rather than character sequences:

Wolfram Language code: spword = SequencePredict[{dq}, FeatureExtractor -> "SegmentedWords"]

Complete the preceding string with 10 consecutive words (spaces and punctuation marks are considered as words):

Wolfram Language code: spword["I", "RandomNextElement" -> 10]

Built-in Sequence Predictors  (1)

Download the "English" built-in sequence predictor:

Wolfram Language code: sp = SequencePredict["English"]

Obtain the log-probability of the given string:

Wolfram Language code: sp["the cat sat on the hat", "SequenceLogProbability"]

Options  (5)

FeatureExtractor  (2)

Preprocess the training text to predict on words rather than at the character level:

Wolfram Language code: sp = SequencePredict[{WikipediaData["computer"]}, FeatureExtractor -> "SegmentedWords"]

Complete the preceding string with 10 consecutive words (spaces and punctuation marks are considered as words):

Wolfram Language code: sp["computer ", "RandomNextElement" -> 10]

Preprocess the training text to lowercase to obtain a better statistic with higher letter counts:

Wolfram Language code: sp = SequencePredict[{WikipediaData["computer"]}, FeatureExtractor -> "LowerCasedText"]

PerformanceGoal  (2)

Train a classifier with an emphasis on the resulting model memory footprint:

Wolfram Language code: sp = SequencePredict[{WikipediaData["usa"]}, PerformanceGoal -> "Memory"]
Wolfram Language code: ByteCount[sp]

Compare with the automatically generated model size:

Wolfram Language code: ByteCount@SequencePredict[{WikipediaData["usa"]}]

Tune the computation time and precision when exploring the full sequence probability space:

Wolfram Language code: sp = SequencePredict[{ExampleData[{"Text", "DonQuixoteIEnglish"}]}]

Favor fast and approximated exploration:

Wolfram Language code: {shortertime, quickprediction} = AbsoluteTiming[sp["This is", "NextSequence" -> 50, PerformanceGoal -> "Speed"]]

Favor more in-depth exploration taking longer computation time:

Wolfram Language code: {longertime, betterprediction} = AbsoluteTiming[sp["This is", "NextSequence" -> 50, PerformanceGoal -> "Quality"]]

Compare the results:

Wolfram Language code: sp[#, "SequenceProbability"]& /@ {quickprediction, betterprediction}

Method  (1)

Specify a memory size of 3 for the Markov process trained on the training subsequences:

Wolfram Language code: sp = SequencePredict[{WikipediaData["computer"]}, Method -> {"Markov", "Order" -> 3}]

Possible Issues  (1)

An empty list is parsed as the list with no sequences inside and will return an empty list:

Wolfram Language code: sp = SequencePredict[{{[image], [image]}, {[image], [image], [image]}, {[image], [image], [image]}, {[image], [image]}}, {}]

To obtain the most likely next element completing an empty sequence, nest it in a second list for disambiguation:

Wolfram Language code: sp = SequencePredict[{{[image], [image]}, {[image], [image], [image]}, {[image], [image], [image]}, {[image], [image]}}, {{}}]
Wolfram Research (2017), SequencePredict, Wolfram Language function, https://reference.wolfram.com/language/ref/SequencePredict.html (updated 2017).

Text

Wolfram Research (2017), SequencePredict, Wolfram Language function, https://reference.wolfram.com/language/ref/SequencePredict.html (updated 2017).

CMS

Wolfram Language. 2017. "SequencePredict." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2017. https://reference.wolfram.com/language/ref/SequencePredict.html.

APA

Wolfram Language. (2017). SequencePredict. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/SequencePredict.html

BibTeX

@misc{reference.wolfram_2026_sequencepredict, author="Wolfram Research", title="{SequencePredict}", year="2017", howpublished="\url{https://reference.wolfram.com/language/ref/SequencePredict.html}", note=[Accessed: 13-August-2026]}

BibLaTeX

@online{reference.wolfram_2026_sequencepredict, organization={Wolfram Research}, title={SequencePredict}, year={2017}, url={https://reference.wolfram.com/language/ref/SequencePredict.html}, note=[Accessed: 13-August-2026]}
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