thanks =) I already tried this… but now something super wired is happening:

I have my model_params from the swarm:

```
MODEL_PARAMS = \
{ 'aggregationInfo': { 'days': 0,
'fields': [],
'hours': 0,
'microseconds': 0,
'milliseconds': 0,
'minutes': 0,
'months': 0,
'seconds': 0,
'weeks': 0,
'years': 0},
'model': 'HTMPrediction',
'modelParams': { 'anomalyParams': { u'anomalyCacheRecords': None,
u'autoDetectThreshold': None,
u'autoDetectWaitRecords': None},
'clParams': { 'alpha': 0.08843107003444935,
'regionName': 'SDRClassifierRegion',
'steps': '1',
'verbosity': 0},
'inferenceType': 'TemporalMultiStep',
'sensorParams': { 'encoders': { '_classifierInput': { 'classifierOnly': True,
'clipInput': True,
'fieldname': 'consumption',
'maxval': 617.76,
'minval': 0.0,
'n': 521,
'name': '_classifierInput',
'type': 'ScalarEncoder',
'w': 21},
u'consumption': None,
u'datetime_dayOfWeek': None,
u'datetime_timeOfDay': { 'fieldname': 'datetime',
'name': 'datetime',
'timeOfDay': ( 21,
3.1050137779268128),
'type': 'DateEncoder'},
u'datetime_weekend': None,
u'temperature': None},
'sensorAutoReset': None,
'verbosity': 0},
'spEnable': True,
'spParams': { 'boostStrength': 0.0,
'columnCount': 2048,
'globalInhibition': 1,
'inputWidth': 0,
'numActiveColumnsPerInhArea': 40,
'potentialPct': 0.8,
'seed': 1956,
'spVerbosity': 0,
'spatialImp': 'cpp',
'synPermActiveInc': 0.05,
'synPermConnected': 0.1,
'synPermInactiveDec': 0.01904419349817022},
'tmEnable': True,
'tmParams': { 'activationThreshold': 15,
'cellsPerColumn': 32,
'columnCount': 2048,
'globalDecay': 0.0,
'initialPerm': 0.21,
'inputWidth': 2048,
'maxAge': 0,
'maxSegmentsPerCell': 128,
'maxSynapsesPerSegment': 32,
'minThreshold': 11,
'newSynapseCount': 20,
'outputType': 'normal',
'pamLength': 5,
'permanenceDec': 0.1,
'permanenceInc': 0.1,
'seed': 1960,
'temporalImp': 'cpp',
'verbosity': 0},
'trainSPNetOnlyIfRequested': False},
'predictAheadTime': None,
'version': 1}
```

Now I’m adding the encoding for the temperature:

```
u'temperature':{ 'clipInput': True,
'fieldname': 'temperature',
'maxval': 25.25,
'minval': -5.25,
'n': 387,
'name': 'temperature',
'type': 'ScalarEncoder',
'w': 21},
```

But my prediction results are terrible now. I am using a crossvalidation bc I want to compare the results of different Models. And now, I have these results for the HTM:

which is basically a naive prediction.

And for another test_set (of the crossvalidation) I have:

So basically a predction of zero all the time.What can be reasons for a behavior like this? Before adding the temperature I had:

So why this strange behavior, when adding informations ( And i know that there is a correlation of -0.9 between temperature and consumption… so it is an important information)

I would really appreciate some help.

Thanks a lot in advance.

Helena