Band Prediction

Band Prediction

In most series, the data follows periodic patterns that can be analyzed separately. Thus, the original series y may be divided in three (or more) new series. For instance, one typical set of new series may be: yline, ylp and yhp. The concept of band prediction is shown in the figure (note that lp stands for low pass and hp for high pass).

Filtering

Tip
It is possible to predict a value for each of the new series, and the total prediction value can be obtained by adding each of the predictions as shown in the figure below.

FilterPrediction

Tip
A prediction problem by removing the trend of the series using band frequencies has five steps as described in the figure below.
  1. Separate the series to create: yline, ylp
    2. and , yhp.
  2. Estimate the line prediction using the equation of the line
  3. Estimate the ANN prediction using prediction analysis for the low pass series
  4. Estimate the ANN prediction using prediction analysis for the high pass series
  5. Combine the three estimates

Problem 1
Create a Neural Lab project called BandPred to predict the temperature in a small city in Europe using band prediction (select the Prediction option in the New Project dialog). Perform STEP 1. (a) Compute the value of m and b of the line y = mx + b that best fits the data. (b) Remove the trend from the original series to create ydetrend.

Solution 1
Add the RemoveTrend.lab file and write the code shown below.

RunRun click the button to execute the code.

GraphGraph click the button to plot the data as shown.

BandPred\RemoveTrend.lab
Vector temperature;
temperature.Load();
int length = temperature.GetSize();

Vector linefit = temperature.LineFit();
double m = linefit[0];
double b = linefit[1];
linefit.Save();

//_____________________ Create a line to display
Vector x;
x.CreateSeries(0, length-1, length);
Vector yline = m*x+b;
//_____________________ Remove the trend
Vector ydetrend = temperature - yline;
ydetrend.Save();

ydetrend

Problem 2
Plot the spectum of ydetrend.

Solution 1
Add the Spectrum.lab file and write the code shown below.

RunRun click the button to execute the code.

GraphGraph click the button to plot the data as shown. As it can be seen from the plot the series has two main frequency components: 0.28 radians (2.8*π/32) and 0.96 radians (9.8*π/32);

BandPred\Spectrum.lab
Vector ydetrend;
ydetrend.Load();
Vector ydetrendSpect = spectrum(ydetrend);

Spectrum

Problem 3
(a) Create a low pass filter with cut frequency of 0.6 radians. (b) Plot the frequency response of the filter.

Solution 3
Add the FilterResponse.lab file and write the code shown below.

RunRun click the button to execute the code.

BandPred\FilterResponse.lab
Vector impulseResponseLP;
impulseResponseLP.CreateLoPassIR(5, 64, 0.6);
Vector frecResponse = spectrum(impulseResponseLP);

ImpulseResponse

FrequencyResponse

Problem 4
Continue with STEP 1. (a) Use the low pass filter to create ylp. (b) Compute yhp by subtracting ylpydetrend.

Solution 4
Add the Filtering.lab file and write the code shown below.

RunRun click the button to execute the code.

BandPred\Filtering.lab
Vector impulseResponseLP;
impulseResponseLP.CreateLoPassIR(5, 64, 0.6);
//_______________________________
Vector ydetrend;
ydetrend.Load();
Vector ylp = ShortConvolution(ydetrend, impulseResponseLP);
Vector yhp = ydetrend - ylp;
ylp.Save();
yhp.Save();

LowPassBand

HighPassBand

Problem 5
Perform STEP 2. Estimate the line prediction using the equation of the line. That is estimate yline[64].

Solution 5
Add the LinePred.lab file and write the code shown below.

RunRun click the button to execute the code.

BandPred\LinePred.lab
Vector temperature;
temperature.Load();
int n = temperature.GetSize();
Vector linefit;
linefit.Load();
double m = linefit[0];
double b = linefit[1];
double yline = m*n+b;

LinePrediction

Problem 6
Perform STEP 3. Estimate yannlp[64] using prediction analysis by writing the PredAnalysisLP.lab file.

MsePredictionAnalysisLP

ValuePredictionAnalysisLP

Problem 7
Perform STEP 4. Estimate yannhp[64] using prediction analysis by writing the PredAnalysisHP.lab file.

MsePredictionAnalysisHP

ValuePredictionAnalysisHP

Problem 8
Perform STEP 5. Combine the line prediction with the ANN prediction to estimate y[64].

Series Length     Actual Value     yline[64]     yannlp[64]     yannhp[64]     Estimate of y[64]  
64y[64]=23.75       

Problem 9
Create the Validation.lab file to perform the prediction validation.

(a) By removing values at the end of the series ydetrend complete the table below with the predicted value using an ANN. (b) Compute the mse between the actual and the predicted values using the 10 values in the table.

Series Length     Actual Value     yline     yannlp     yannhp     Estimate of y  
63y[63]=21.46       
62y[62]=18.52       
61y[61]=18.17       
60y[60]=19.46       
59y[59]=20.27       
58y[58]=18.40       
57y[57]=15.14       
56y[56]=11.91       
55y[55]=11.91       
54y[54]=13.64       

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