"time series prediction"
Hi everyone
I am new user of RapidMiner and this is my first post, I have 11 months electric feeder load time series data so I want to forecast one day ahead feeder load with the help of this data.so can anyone guide me how can I do this with the help of RapidMiner ?
Data Format:-
date hour1 hour2 hour3 hour4 hour5 hour6 ......... hour24
10/01/2010 .2934 .1983 .1328 .2032 .1002 .1834 ......... .2903
10/02/2010 .2367 . 1298 .1289 .1901 .1192 .1920 ........ .1902
................. ................................................................................
280 days 24 hour
Thanks
Vikas Gupta
I am new user of RapidMiner and this is my first post, I have 11 months electric feeder load time series data so I want to forecast one day ahead feeder load with the help of this data.so can anyone guide me how can I do this with the help of RapidMiner ?
Data Format:-
date hour1 hour2 hour3 hour4 hour5 hour6 ......... hour24
10/01/2010 .2934 .1983 .1328 .2032 .1002 .1834 ......... .2903
10/02/2010 .2367 . 1298 .1289 .1901 .1192 .1920 ........ .1902
................. ................................................................................
280 days 24 hour
Thanks
Vikas Gupta
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Dear Vikas,
please post your questions only once in the most appropriate board and not in every board here. Thanks.
Cheers,
Ingo
please post your questions only once in the most appropriate board and not in every board here. Thanks.
Cheers,
Ingo
Actually I have no idea how to convert this data to 1 column, using rapidminer, so I'm gonna use VIM or python.
Like:
loaddata #<-- this is a comment
0.5144 #<-- first data point
0.5144
0.5144
0.6001
0.6001
0.6859
0.6859
0.7716
0.7716
1.286
1.286
1.286
1.2003
1.2003
1.2003
1.286
1.286
1.5432
1.8004
1.6289
1.5432
1.3717
1.1145
0.8573
0.9431 #<-- day 2
...
1.286 #<-- last data point, etc
edit:
okay here is the data I will be using:
Like:
loaddata #<-- this is a comment
0.5144 #<-- first data point
0.5144
0.5144
0.6001
0.6001
0.6859
0.6859
0.7716
0.7716
1.286
1.286
1.286
1.2003
1.2003
1.2003
1.286
1.286
1.5432
1.8004
1.6289
1.5432
1.3717
1.1145
0.8573
0.9431 #<-- day 2
...
1.286 #<-- last data point, etc
edit:
okay here is the data I will be using:
load
0.5144
0.5144
0.5144
0.6001
0.6001
0.6859
0.6859
0.7716
0.7716
1.286
1.286
1.286
1.2003
1.2003
1.2003
1.286
1.286
1.5432
1.8004
1.6289
1.5432
1.3717
1.1145
0.8573
0.9431
0.6859
0.6859
0.6859
0.7716
0.7716
0
0
0
1.6289
1.3717
1.3717
1.3717
0
1.5432
1.4575
1.4575
1.6289
1.8004
1.7147
1.5432
1.3717
1.1145
0.8573
0.7716
0.6859
0.6859
0.7716
0.9431
1.0288
2.0288
1.2003
1.286
1.286
1.4575
1.3717
1.3717
0
1.286
1.4575
1.5432
1.5432
1.8004
1.8004
1.5432
1.3717
1.2003
0.9431
0.8573
0.7716
0.7716
0.8573
0.8573
0.9431
1.0288
1.2003
1.286
1.3717
1.4575
1.3717
1.286
1.2003
1.286
0
0
1.7147
1.8861
1.8861
1.6289
1.3717
1.0288
0.7716
0.6859
0.6001
0.6001
0.6859
0.7716
0.8573
0
0
1.8861
1.5432
1.5432
1.5432
1.5432
1.286
1.3717
1.3717
1.5432
1.8004
1.8004
1.7147
1.5432
1.3717
1.1145
0.9431
0.8573
0.7716
0.6859
0.6859
0.7716
0.9431
1.0288
1.2003
1.2003
1.3717
1.4575
1.6289
1.5432
0
0
0
0
1.3717
1.3717
1.3717
1.2003
1.0288
0.8573
0.6859
0.6001
0.5144
0.5144
0.6001
0.6859
0.6859
0
0.8573
0.9431
0.9431
0.9431
0.7716
0.7716
0
0
0
0.8573
1.0288
1.1145
1.1145
1.0288
0.9431
0.7716
0.6001
0.6859
0.6001
0.6001
0.6001
0.6859
0.6859
0.7716
0.8573
1.0288
1.0288
1.5432
1.0288
0
0
1.286
1.1145
1.1145
1.286
1.3717
1.286
1.1145
1.0288
0.8573
0.6001
0.5144
0.5144
0.5144
0.5144
0.6001
0.6859
0
0.9431
1.1145
1.0288
1.1145
1.0288
0
0
1.286
1.2003
1.1145
1.3717
1.3717
1.286
1.1145
0.9431
0.8573
0.6001
0.5144
0.5144
0.5144
0.5144
0.6001
0.6859
0
0
1.3717
1.1145
1.1145
1.0288
0
0
1.2003
1.1145
1.1145
1.2003
1.286
1.1145
0.9431
0.9431
0.8573
0.5144
0.4287
0.4287
0.4287
0.4287
0.5144
0.6001
0.6859
0.7716
0.8573
0.8573
0.9431
0.9431
0.8573
0
0.9431
0.9431
0.9431
1.2003
1.286
1.2003
1.1145
0.9431
1.0288
0.7716
0.7716
0.6859
0.6859
0.6859
0.7716
0.9431
0
1.286
1.286
1.286
1.3717
1.4575
1.286
1.2003
1.286
1.286
1.5432
1.7147
1.8861
1.7147
1.6289
1.3717
1.2003
0.8573
0.7716
0.6859
0.6859
0.6859
0.7716
0.8573
0.9431
1.1145
1.286
1.3717
1.4575
1.3717
0
1.6289
1.3717
1.286
0.9431
1.1145
1.1145
1.0288
1.0288
0.9431
0.6001
0.5144
0.5144
0.5144
0.5144
0.5144
0.5144
0.5144
0.5144
0.6859
0.6859
0.7716
0.7716
0.6859
0.6859
0.6001
0.6001
0.6001
0.6001
0.8573
1.0288
0.9431
0.9431
0.7716
0.6001
0.5144
0.3429
0.3429
0.3429
0.3429
0.5144
0.6859
0.6859
0.8573
0.8573
0.8573
0.9431
0.9431
0.8573
0.8573
0.8573
0.7716
0.8573
0.9431
1.0288
1.0288
0.9431
0.7716
0.6859
0.5144
0.3429
0.3429
0.3429
0.3429
0.5144
0.5144
0.6859
0.7716
0.8573
0.8573
1.1145
1.2003
1.2003
1.2003
1.2003
1.2003
1.286
1.5432
1.6289
1.5432
1.286
1.1145
0.9431
0.6859
0.5144
0.5144
0.5144
0.6859
0.8573
0.8573
0.9431
1.2003
1.1145
1.2003
1.286
1.3717
0
0
1.3717
1.3717
1.4575
1.7147
1.8004
1.7147
1.3717
1.2003
0.9431
0.7716
0.6859
0.6001
0.6001
0.6001
0.6859
0.7716
1.0288
1.1145
1.2003
1.286
1.4575
1.4575
0
0
1.1145
1.3717
1.5432
1.7147
1.8861
1.6289
1.286
1.1145
0.9431
0.7716
0.6859
0.5144
0.5144
0.5144
0.6859
0.8573
0.9431
1.0288
1.1145
1.2003
1.3717
1.3717
1.286
1.2003
1.286
1.286
1.3717
1.6289
1.7147
1.6289
1.3717
1.1145
1.0288
0.6859
0.5144
0.5144
0.5144
0.5144
0.6859
0.8573
0.8573
1.1145
1.1145
1.2003
1.3717
1.3717
0
1.0288
1.3717
1.2003
1.286
1.5432
1.6289
1.4575
1.286
1.1145
1.0288
0.7716
0.6859
0.6859
0.6859
0.6859
0.6859
0.7716
0.8573
0.9431
1.1145
1.0288
1.1145
1.0288
0.9431
0.7716
0.8573
0.8573
0.8573
1.1145
1.286
1.286
1.1145
1.0288
0.8573
0.7716
0.6859
0.6001
0.6001
0.6859
0.7716
0.8573
1.0288
1.0288
1.0288
1.1145
1.0288
1.0288
0
1.286
1.2003
1.2003
1.2003
1.5432
1.5432
1.4575
1.286
1.1145
0.9431
0.6859
0.6859
0.6001
0.6001
0.6859
0.7716
0.8573
0.9431
1.1145
1.1145
1.2003
1.286
1.286
0
1.2003
1.2003
1.2003
1.3717
1.6289
1.6289
1.5432
1.286
1.1145
0.8573
0.7716
0.6001
0.6001
0.6001
0.6859
0.7716
0.8573
0.9431
1.2003
1.1145
1.1145
1.2003
1.3717
0
1.286
1.2003
1.286
1.3717
1.6289
1.8861
1.8004
1.4575
1.2003
0.8573
0.6859
0.6001
0.6001
0.6001
0.6859
0.8573
0.8573
0.9431
1.1145
1.1145
1.1145
0
0
0
0
0
0
1.7147
1.8004
1.8861
1.5432
1.3717
1.1145
0.7716
0.6859
0.6001
0.6001
0.6001
0.6859
0.6859
0.7716
1.0288
1.1145
1.1145
1.2003
1.2003
1.2003
1.2003
1.0288
1.0288
1.1145
1.3717
1.5432
1.6289
1.5432
1.5432
1.286
1.1145
0.8573
0.6001
0.5144
0.5144
0.5144
0.6859
0.7716
0.9431
1.1145
1.2003
1.2003
1.3717
1.3717
1.2003
1.1145
1.1145
1.2003
1.286
1.5432
1.6289
1.4575
1.286
1.1145
0.8573
0.6859
0.5144
0.5144
0.5144
0.5144
0.6859
0.8573
1.1145
1.3717
1.6289
1.6289
1.6289
1.4575
1.4575
1.4575
1.3717
1.3717
1.4575
1.8861
1.9719
1.9719
1.6289
1.6289
1.3717
1.2003
0.9431
0.8573
0.8573
0.8573
0.8573
1.2003
1.3717
1.4575
1.5432
1.8861
2.1434
2.1434
2.1434
1.9719
2.0576
1.2003
2.0576
2.572
2.572
2.3148
1.8861
1.7147
1.3717
1.3717
1.1145
1.1145
0
0.8573
0.9431
1.2003
1.286
1.5432
1.6289
1.8004
2.1434
2.1434
2.0576
1.8861
2.0576
2.0576
2.2291
2.4006
2.4863
2.3148
1.9719
1.7147
1.4575
1.286
0.9431
0.9431
0.8573
0.8573
0.9431
1.1145
1.3717
1.4575
1.6289
1.8004
2.1434
2.1434
2.1434
1.9719
1.9719
2.1434
2.1434
2.4006
2.4863
2.3148
2.0576
1.7147
1.5432
1.1145
1.0288
0.9431
0.9431
0.8573
1.0288
1.2003
1.3717
1.5432
1.6289
1.8861
2.1434
2.0576
2.0576
0
2.0576
2.0576
2.0576
2.4006
2.4006
2.2291
1.9719
1.7147
1.4575
1.2003
0.9431
0.8573
0.8573
0.8573
0.8573
1.2003
1.3717
1.5432
1.5432
1.8004
2.1434
2.0576
2.0576
1.9719
1.8861
2.0576
2.0576
2.3148
2.4006
2.2291
1.7147
1.8004
1.3717
1.1145
0.9431
0.8573
0.8573
0.8573
0.8573
1.1145
1.2003
1.4575
1.5432
1.7147
2.1434
2.0576
1.9719
1.8004
1.8861
1.8861
1.8861
2.2291
2.3148
2.2291
2.1434
1.6289
1.3717
1.0288
0.8573
0.8573
0.7716
0.7716
0.7716
0.9431
1.1145
1.286
1.3717
1.5432
1.6289
1.5432
1.3717
1.286
1.2003
1.2003
1.286
1.6289
1.8004
1.8004
1.6289
1.3717
1.2003
1.0288
0.8573
0.8573
0.8573
1.0288
1.2003
1.286
1.286
1.3717
1.5432
1.7147
1.8861
1.9719
1.8004
1.8004
1.7147
1.8004
1.8861
2.0576
2.2291
2.0576
1.7147
1.5432
1.2003
1.0288
0.8573
0.8573
0.8573
0.8573
1.0288
1.2003
1.286
1.4575
1.4575
1.6289
1.8004
1.7147
1.6289
1.6289
1.6289
1.7147
1.8004
1.9719
2.3148
2.2291
1.8861
1.5432
1.286
0.9431
0.8573
0.7716
0.7716
0.7716
0.8573
1.1145
1.286
1.4575
1.5432
1.7147
1.8004
1.8861
1.8004
1.8004
1.8861
1.8861
1.9719
2.3148
2.572
2.3148
1.8004
1.6289
1.3717
1.0288
0.8573
0.8573
0.8573
0.8573
1.0288
1.2003
1.2003
1.4575
1.6289
1.7147
1.8861
1.8861
1.8004
1.8861
1.8861
1.8004
0
2.4006
2.3148
2.0576
1.8004
1.6289
1.286
1.0288
0.9431
0.8573
0.6859
0.6859
0.8573
1.2003
1.286
1.4575
1.6289
1.8004
1.9719
2.1434
1.9719
1.8861
1.8861
1.9719
2.0576
2.3148
2.4863
2.1434
1.9719
1.8004
1.4575
1.1145
0.8573
0.7716
0.7716
0.7716
0.8573
1.0288
1.2003
1.4575
1.4575
1.8004
1.8861
1.8861
1.8004
1.7147
1.6289
1.7147
1.7147
2.0576
2.3148
2.1434
1.8861
1.5432
1.3717
1.1145
0.8573
0.7716
0.9431
0.9431
1.0288
1.1145
1.3717
1.7147
1.8004
2.0576
1.6289
1.4575
1.3717
1.286
1.2003
0
1.286
1.8004
1.8004
1.8861
1.6289
1.4575
1.4575
1.1145
0.8573
0.7716
0.7716
0.7716
0.7716
1.0288
1.286
1.3717
1.5432
1.8861
2.0576
2.1434
2.0576
1.8861
1.8861
1.8861
1.9719
2.3148
2.2291
2.1434
1.9719
1.6289
1.4575
1.0288
0.8573
0.8573
0.7716
0.7716
0.7716
1.0288
1.286
1.5432
1.7147
1.8861
2.0576
2.0576
1.8861
0
1.9719
1.9719
2.0576
2.2291
2.2291
2.0576
1.8861
1.6289
1.3717
1.0288
0.8573
0.7716
0.7716
0.7716
0.7716
0.9431
1.286
1.3717
1.4575
1.7147
1.8861
1.8861
1.8861
1.7147
1.7147
1.7147
1.8861
2.1434
2.3148
2.0576
1.8004
1.5432
1.286
0.9431
0.8573
0.7716
0.7716
0.7716
0.7716
1.0288
1.2003
1.3717
1.5432
1.8004
1.8004
1.8861
1.8004
1.7147
1.7147
1.7147
2.2291
2.2291
2.2291
2.0576
1.8004
1.4575
1.2003
0.9431
0.7716
0.7716
0.7716
0.9431
0.9431
1.2003
1.4575
1.8004
1.8004
2.1434
2.1434
2.2291
2.2291
2.3148
2.2291
2.1434
1.8004
2.2291
2.3148
1.9719
1.7147
1.4575
1.286
1.0288
0.7716
0.7716
0.6859
0.6859
0.7716
0.9431
1.2003
1.3717
1.6289
1.8004
2.4006
2.4006
2.3148
2.2291
2.2291
2.4006
2.4006
2.7435
2.7435
2.6578
2.3148
1.9719
1.6289
1.2003
1.0288
1.0288
0.9431
1.0288
1.1145
1.2003
1.3717
1.5432
1.8004
1.8861
1.8004
1.7147
1.5432
1.6289
1.5432
1.4575
1.5432
1.9719
2.3148
2.2291
1.9719
1.8861
1.6289
1.2003
1.0288
0.9431
0.9431
0.9431
1.1145
1.3717
1.5432
1.8004
1.8861
2.1434
2.3148
2.2291
2.2291
0
2.1434
2.1434
2.2291
2.572
2.7435
2.572
2.3148
2.0576
1.7147
1.286
Thanks for help me Wessel
Please help me about windowing operator(horizon,window size) to forecast the feeder load one day ahead.
Thanks
Vikas
Please help me about windowing operator(horizon,window size) to forecast the feeder load one day ahead.
Thanks
Vikas
Your problem does not seem to be really interesting.
So your better of with classical statistics. No need for windowing, embedding, and machine learning here.
http://devio.us/~wessel/load/load.jpeg
http://devio.us/~wessel/load/load2.jpeg
So your better of with classical statistics. No need for windowing, embedding, and machine learning here.
http://devio.us/~wessel/load/load.jpeg
http://devio.us/~wessel/load/load2.jpeg
Actually I am trying to build a model where I can predict the load in advance(1 or 2 day ahead) with the help of previous load data which can improve load shedding management of electric feeder.
Meh, if you insist creating a model using heavy number crunching machine learning....
here is the process:
here is the process:
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.0">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.0.9" expanded="true" name="Process">
<parameter key="logfile" value="/home/wessel/loaddata.aml"/>
<process expanded="true" height="507" width="705">
<operator activated="true" class="read_csv" compatibility="5.0.9" expanded="true" height="60" name="Read CSV" width="90" x="45" y="30">
<parameter key="file_name" value="/home/wessel/Desktop/loaddata.csv"/>
<parameter key="column_separators" value=" "/>
<parameter key="date_format" value="MM/dd/yyyy"/>
<list key="data_set_meta_data_information"/>
</operator>
<operator activated="false" class="series:moving_average" compatibility="5.0.2" expanded="true" height="76" name="MA_3_cen" width="90" x="180" y="30">
<parameter key="attribute_name" value="load"/>
<parameter key="window_width" value="3"/>
<parameter key="result_position" value="center"/>
<parameter key="keep_original_attribute" value="false"/>
</operator>
<operator activated="false" class="rename" compatibility="5.0.9" expanded="true" height="76" name="Rename" width="90" x="315" y="30">
<parameter key="old_name" value="moving_average(load)"/>
<parameter key="new_name" value="ma3_load"/>
</operator>
<operator activated="true" class="filter_examples" compatibility="5.0.9" expanded="true" height="76" name="Filter Examples" width="90" x="450" y="30">
<parameter key="condition_class" value="no_missing_attributes"/>
</operator>
<operator activated="true" class="series:windowing" compatibility="5.0.2" expanded="true" height="76" name="Windowing" width="90" x="585" y="30">
<parameter key="horizon" value="24"/>
<parameter key="window_size" value="24"/>
<parameter key="create_label" value="true"/>
<parameter key="label_attribute" value="load"/>
</operator>
<operator activated="false" class="principal_component_analysis" compatibility="5.0.9" expanded="true" height="94" name="PCA" width="90" x="45" y="120">
<parameter key="dimensionality_reduction" value="fixed number"/>
<parameter key="number_of_components" value="4"/>
</operator>
<operator activated="true" class="optimize_selection" compatibility="5.0.9" expanded="true" height="94" name="Optimize Selection" width="90" x="180" y="120">
<parameter key="generations_without_improval" value="2"/>
<parameter key="keep_best" value="2"/>
<process expanded="true" height="507" width="784">
<operator activated="true" class="series:sliding_window_validation" compatibility="5.0.2" expanded="true" height="112" name="Validation" width="90" x="186" y="163">
<parameter key="training_window_width" value="24"/>
<parameter key="test_window_width" value="24"/>
<parameter key="horizon" value="24"/>
<parameter key="cumulative_training" value="true"/>
<process expanded="true" height="507" width="165">
<operator activated="true" class="linear_regression" compatibility="5.0.9" expanded="true" height="94" name="Linear Regression" width="90" x="45" y="30">
<parameter key="feature_selection" value="none"/>
</operator>
<connect from_port="training" to_op="Linear Regression" to_port="training set"/>
<connect from_op="Linear Regression" from_port="model" to_port="model"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
<process expanded="true" height="507" width="300">
<operator activated="true" class="apply_model" compatibility="5.0.9" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_regression" compatibility="5.0.9" expanded="true" height="76" name="Performance" width="90" x="45" y="120">
<parameter key="root_mean_squared_error" value="false"/>
<parameter key="correlation" value="true"/>
</operator>
<connect from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="averagable 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<connect from_port="example set" to_op="Validation" to_port="training"/>
<connect from_op="Validation" from_port="averagable 1" to_port="performance"/>
<portSpacing port="source_example set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_performance" spacing="0"/>
</process>
</operator>
<operator activated="true" class="select_by_weights" compatibility="5.0.9" expanded="true" height="94" name="Select by Weights" width="90" x="315" y="120"/>
<operator activated="true" class="series:sliding_window_validation" compatibility="5.0.2" expanded="true" height="112" name="Validation2" width="90" x="450" y="120">
<parameter key="training_window_width" value="24"/>
<parameter key="test_window_width" value="24"/>
<parameter key="horizon" value="24"/>
<parameter key="cumulative_training" value="true"/>
<process expanded="true" height="507" width="300">
<operator activated="true" class="linear_regression" compatibility="5.0.9" expanded="true" height="94" name="LinearR2" width="90" x="180" y="30">
<parameter key="feature_selection" value="none"/>
</operator>
<connect from_port="training" to_op="LinearR2" to_port="training set"/>
<connect from_op="LinearR2" from_port="model" to_port="model"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
<process expanded="true" height="507" width="300">
<operator activated="true" class="apply_model" compatibility="5.0.9" expanded="true" height="76" name="ApplyM2" width="90" x="45" y="30">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="write_aml" compatibility="5.0.9" expanded="true" height="60" name="Write AML" width="90" x="60" y="160">
<parameter key="example_set_file" value="/home/wessel/loaddata.dat"/>
<parameter key="attribute_description_file" value="/home/wessel/loaddata.aml"/>
</operator>
<operator activated="true" class="performance_regression" compatibility="5.0.9" expanded="true" height="76" name="Perf2" width="90" x="45" y="300">
<parameter key="root_mean_squared_error" value="false"/>
<parameter key="correlation" value="true"/>
</operator>
<connect from_port="model" to_op="ApplyM2" to_port="model"/>
<connect from_port="test set" to_op="ApplyM2" to_port="unlabelled data"/>
<connect from_op="ApplyM2" from_port="labelled data" to_op="Write AML" to_port="input"/>
<connect from_op="Write AML" from_port="through" to_op="Perf2" to_port="labelled data"/>
<connect from_op="Perf2" from_port="performance" to_port="averagable 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="read_aml" compatibility="5.0.9" expanded="true" height="60" name="Read AML" width="90" x="447" y="300">
<parameter key="attributes" value="/home/wessel/loaddata.aml"/>
<parameter key="column_separators" value=" "/>
</operator>
<connect from_op="Read CSV" from_port="output" to_op="Filter Examples" to_port="example set input"/>
<connect from_op="Filter Examples" from_port="example set output" to_op="Windowing" to_port="example set input"/>
<connect from_op="Windowing" from_port="example set output" to_op="Optimize Selection" to_port="example set in"/>
<connect from_op="Optimize Selection" from_port="example set out" to_op="Select by Weights" to_port="example set input"/>
<connect from_op="Optimize Selection" from_port="weights" to_op="Select by Weights" to_port="weights"/>
<connect from_op="Select by Weights" from_port="example set output" to_op="Validation2" to_port="training"/>
<connect from_op="Select by Weights" from_port="original" to_port="result 3"/>
<connect from_op="Validation2" from_port="model" to_port="result 1"/>
<connect from_op="Validation2" from_port="averagable 1" to_port="result 4"/>
<connect from_op="Read AML" from_port="output" to_port="result 2"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="144"/>
<portSpacing port="sink_result 2" spacing="0"/>
<portSpacing port="sink_result 3" spacing="0"/>
<portSpacing port="sink_result 4" spacing="0"/>
<portSpacing port="sink_result 5" spacing="0"/>
</process>
</operator>
</process>
Result:
correlation: 0.795 +/- 0.136 (mikro: 0.786)
correlation: 0.795 +/- 0.136 (mikro: 0.786)
If we see the data there are many outlier(0 and equal load) or human intervention so for better result should I perform outlier analysis before the forecasting ?
I don't know, it depends on your application.
A correlation of 0.8 is already really good.
Depends also on how much noise your sensor has.
A correlation of 0.8 is already really good.
Depends also on how much noise your sensor has.
So, ehm, you got the "process" to run?
Dear Wessel
I got the process but please give me some help abut it's Output
1:- Prediction trend accuracy and correlation both are same thing?
2:-Can you give me some explanation about its output of process?
Thanks
Vikas
I got the process but please give me some help abut it's Output
1:- Prediction trend accuracy and correlation both are same thing?
2:-Can you give me some explanation about its output of process?
Thanks
Vikas
No they are not the same, but they are related.
Look at the scatter plot of predicted load vs actual load.
When a data point is predicted correctly it lies exactly on the diagonal.
You see that all data points that are not 0 are predicted with only a small error.
The error is bigger in data points that are 0, which is expected because they are anomalous values.
I could have used "mean absolute error" instead of "correlation".
But the nice thing about "correlation" is that its invariant to the dataset.
If I would multiply all data points by a factor 100, "mean absolute error" would go up by a factor 100.
Correlation stays the same, since its normalized between -1 and 1.
Look at the scatter plot of predicted load vs actual load.
When a data point is predicted correctly it lies exactly on the diagonal.
You see that all data points that are not 0 are predicted with only a small error.
The error is bigger in data points that are 0, which is expected because they are anomalous values.
I could have used "mean absolute error" instead of "correlation".
But the nice thing about "correlation" is that its invariant to the dataset.
If I would multiply all data points by a factor 100, "mean absolute error" would go up by a factor 100.
Correlation stays the same, since its normalized between -1 and 1.
Hi Wessel
Can you help me about this linear regression generated by process
0.299 * load-23 - 0.041 * load-19 + 0.006 * load-15 - 0.007 * load-8 - 0.014 * load-5 + 0.217 * load-1 + 0.407 * load-0 + 0.182
for forecasting of one day ahead load.
Can you help me about this linear regression generated by process
0.299 * load-23 - 0.041 * load-19 + 0.006 * load-15 - 0.007 * load-8 - 0.014 * load-5 + 0.217 * load-1 + 0.407 * load-0 + 0.182
for forecasting of one day ahead load.
What you want to know about this?
this is regression equation so how can I forecast(calculate) of load at 12,11,10,7 hour can you show me one example?
I'm not sure I understand what you are asking.
Can you suggest me any other alternative for prediction of (one day load) with the help of previous model ? 
An alternative would be: Arima:
http://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average
http://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average
Can I apply ARIMA using RapidMiner operators for forecasting the load?
No.
Hi Wessel
Can you give me some idea about one hour ahead load prediction using same data set ?
Thanks
Can you give me some idea about one hour ahead load prediction using same data set ?
Thanks
I gave you a full implementation in Rapid Miner, and a link to alternative approach,...
isn't that ideas enough?
isn't that ideas enough?
day_time, load
1, .2934
2, .1983
....
n .1902
Then use the windowing operator with the appropriate embedding dimension.
Then use k-nn or linear regression as a learner.
If you upload like 50 rows of data I'll make you an example process.