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How do I predict the next ten numbers (price) based on my data?
Learner I
Hello
How are you all?
First of all, I did a lot of searching in the forum, but I did not get the desired result. I am a beginner in this field. And I really need the help of the great professors of the association. Because it is an important issue for me.
I have some data with 6 numeric properties with the names of the price date, etc., one of which is my class. In the name of price
I want to use this data to predict the next ten data. I know I have to use time series or linear regression, but in RapidMiner I do not know what it is like?
Can anyone help me? Do you have a sample?
This is an example of my process.
But only for current data. I have a prediction. And I do not know what it will be like in the future
<? xml version = "1.0" encoding = "UTF-8"?> <process version = "8.1.003">
<context>
<input />
<output />
<macros />
</context>
<operator activated = "true" class = "process" compatibility = "8.1.003" expanded = "true" name = "Process">
<process expanded = "true">
<operator activated = "true" class = "read_excel" compatibility = "8.1.003" expanded = "true" height = "68" name = "Read Excel" width = "90" x = "45" y = "34" >
<parameter key = "excel_file" value = "C: \ mydata.xlsx" />
<list key = "annotations" />
<list key = "data_set_meta_data_information" />
</operator>
<operator activated = "true" class = "nominal_to_numerical" compatibility = "8.1.003" expanded = "true" height = "103" name = "Nominal to Numerical (2)" width = "90" x = "45" y = "136">
<list key = "comparison_groups" />
</operator>
<operator activated = "true" class = "nominal_to_numerical" compatibility = "8.1.003" expanded = "true" height = "103" name = "Nominal to Numerical" width = "90" x = "45" y = "289 ">
<list key = "comparison_groups" />
</operator>
<operator activated = "true" class = "select_attributes" compatibility = "8.1.003" expanded = "true" height = "82" name = "Select Attributes" width = "90" x = "179" y = "289" />
<operator activated = "true" class = "set_role" compatibility = "8.1.003" expanded = "true" height = "82" name = "Set Role" width = "90" x = "313" y = "187" >
<parameter key = "attribute_name" value = "Close" />
<parameter key = "target_role" value = "label" />
<list key = "set_additional_roles" />
</operator>
<operator activated = "true" class = "replace_missing_values" compatibility = "8.1.003" expanded = "true" height = "103" name = "Replace Missing Values" width = "90" x = "313" y = "34 ">
<list key = "columns" />
</operator>
<operator activated = "true" class = "split_data" compatibility = "8.1.003" expanded = "true" height = "103" name = "Split Data" width = "90" x = "447" y = "187" >
<enumeration key = "partitions">
<parameter key = "ratio" value = "0.9" />
<parameter key = "ratio" value = "0.1" />
</enumeration>
<parameter key = "sampling_type" value = "linear sampling" />
</operator>
<operator activated = "true" class = "neural_net" compatibility = "8.1.003" expanded = "true" height = "82" name = "Neural Net" width = "90" x = "447" y = "34" >
<list key = "hidden_layers" />
</operator>
<operator activated = "true" class = "apply_model" compatibility = "8.1.003" expanded = "true" height = "82" name = "Apply Model" width = "90" x = "648" y = "85" >
<list key = "application_parameters" />
</operator>
<connect from_op = "Read Excel" from_port = "output" to_op = "Nominal to Numerical (2)" to_port = "example set input" />
<connect from_op = "Nominal to Numerical (2)" from_port = "example set output" to_op = "Nominal to Numerical" to_port = "example set input" />
<connect from_op = "Nominal to Numerical" from_port = "example set output" to_op = "Select Attributes" to_port = "example set input" />
<connect from_op = "Select Attributes" from_port = "example set output" to_op = "Set Role" to_port = "example set input" />
<connect from_op = "Set Role" from_port = "example set output" to_op = "Replace Missing Values" to_port = "example set input" />
<connect from_op = "Replace Missing Values" from_port = "example set output" to_op = "Split Data" to_port = "example set" />
<connect from_op = "Split Data" from_port = "partition 1" to_op = "Neural Net" to_port = "training set" />
<connect from_op = "Split Data" from_port = "partition 2" to_op = "Apply Model" to_port = "unlabelled data" />
<connect from_op = "Neural Net" from_port = "model" to_op = "Apply Model" to_port = "model" />
<connect from_op = "Apply Model" from_port = "labeled data" to_port = "result 1" />
<connect from_op = "Apply Model" from_port = "model" to_port = "result 2" />
<portSpacing port = "source_input 1" spacing = "0" />
<portSpacing port = "sink_result 1" spacing = "0" />
<portSpacing port = "sink_result 2" spacing = "0" />
<portSpacing port = "sink_result 3" spacing = "0" />
</process>
</operator>
</process>
How are you all?
First of all, I did a lot of searching in the forum, but I did not get the desired result. I am a beginner in this field. And I really need the help of the great professors of the association. Because it is an important issue for me.
I have some data with 6 numeric properties with the names of the price date, etc., one of which is my class. In the name of price
I want to use this data to predict the next ten data. I know I have to use time series or linear regression, but in RapidMiner I do not know what it is like?
Can anyone help me? Do you have a sample?
This is an example of my process.
But only for current data. I have a prediction. And I do not know what it will be like in the future
<? xml version = "1.0" encoding = "UTF-8"?> <process version = "8.1.003">
<context>
<input />
<output />
<macros />
</context>
<operator activated = "true" class = "process" compatibility = "8.1.003" expanded = "true" name = "Process">
<process expanded = "true">
<operator activated = "true" class = "read_excel" compatibility = "8.1.003" expanded = "true" height = "68" name = "Read Excel" width = "90" x = "45" y = "34" >
<parameter key = "excel_file" value = "C: \ mydata.xlsx" />
<list key = "annotations" />
<list key = "data_set_meta_data_information" />
</operator>
<operator activated = "true" class = "nominal_to_numerical" compatibility = "8.1.003" expanded = "true" height = "103" name = "Nominal to Numerical (2)" width = "90" x = "45" y = "136">
<list key = "comparison_groups" />
</operator>
<operator activated = "true" class = "nominal_to_numerical" compatibility = "8.1.003" expanded = "true" height = "103" name = "Nominal to Numerical" width = "90" x = "45" y = "289 ">
<list key = "comparison_groups" />
</operator>
<operator activated = "true" class = "select_attributes" compatibility = "8.1.003" expanded = "true" height = "82" name = "Select Attributes" width = "90" x = "179" y = "289" />
<operator activated = "true" class = "set_role" compatibility = "8.1.003" expanded = "true" height = "82" name = "Set Role" width = "90" x = "313" y = "187" >
<parameter key = "attribute_name" value = "Close" />
<parameter key = "target_role" value = "label" />
<list key = "set_additional_roles" />
</operator>
<operator activated = "true" class = "replace_missing_values" compatibility = "8.1.003" expanded = "true" height = "103" name = "Replace Missing Values" width = "90" x = "313" y = "34 ">
<list key = "columns" />
</operator>
<operator activated = "true" class = "split_data" compatibility = "8.1.003" expanded = "true" height = "103" name = "Split Data" width = "90" x = "447" y = "187" >
<enumeration key = "partitions">
<parameter key = "ratio" value = "0.9" />
<parameter key = "ratio" value = "0.1" />
</enumeration>
<parameter key = "sampling_type" value = "linear sampling" />
</operator>
<operator activated = "true" class = "neural_net" compatibility = "8.1.003" expanded = "true" height = "82" name = "Neural Net" width = "90" x = "447" y = "34" >
<list key = "hidden_layers" />
</operator>
<operator activated = "true" class = "apply_model" compatibility = "8.1.003" expanded = "true" height = "82" name = "Apply Model" width = "90" x = "648" y = "85" >
<list key = "application_parameters" />
</operator>
<connect from_op = "Read Excel" from_port = "output" to_op = "Nominal to Numerical (2)" to_port = "example set input" />
<connect from_op = "Nominal to Numerical (2)" from_port = "example set output" to_op = "Nominal to Numerical" to_port = "example set input" />
<connect from_op = "Nominal to Numerical" from_port = "example set output" to_op = "Select Attributes" to_port = "example set input" />
<connect from_op = "Select Attributes" from_port = "example set output" to_op = "Set Role" to_port = "example set input" />
<connect from_op = "Set Role" from_port = "example set output" to_op = "Replace Missing Values" to_port = "example set input" />
<connect from_op = "Replace Missing Values" from_port = "example set output" to_op = "Split Data" to_port = "example set" />
<connect from_op = "Split Data" from_port = "partition 1" to_op = "Neural Net" to_port = "training set" />
<connect from_op = "Split Data" from_port = "partition 2" to_op = "Apply Model" to_port = "unlabelled data" />
<connect from_op = "Neural Net" from_port = "model" to_op = "Apply Model" to_port = "model" />
<connect from_op = "Apply Model" from_port = "labeled data" to_port = "result 1" />
<connect from_op = "Apply Model" from_port = "model" to_port = "result 2" />
<portSpacing port = "source_input 1" spacing = "0" />
<portSpacing port = "sink_result 1" spacing = "0" />
<portSpacing port = "sink_result 2" spacing = "0" />
<portSpacing port = "sink_result 3" spacing = "0" />
</process>
</operator>
</process>
0
Answers
Welcome in the community and welcome in the world of data science and time series.
To start doing time series analysis, you can have a look at this time series course in our academy: https://academy.rapidminer.com/courses/time-series-analytics
Its free to use, just use your RapidMiner account.
Also you can check out this video: https://www.youtube.com/watch?v=Hvdh8ItfiGA
And finally you can check out all Time Series operators in RapidMiner by just typing "Time Series" in the operator panel of RapidMiner. Check the help texts and the tutorial processes for the operators to learn how to do time series analysis. There are also some template processes und Samples/Time Series/templates in the repository panel.
Please keep in mind that learning data science and time series analysis is not a 1 day activity. Take your time exploring the concepts, start simple and not try to get the best predictions for the future, but first understand how things work.
Hopes this helps,
Best regards,
Fabian