I am applying ARIMA

Hi all,

I have time series data.

My input series is Temperature, humidity, pressure

And i want to predict wind speed one day ahead, so my target series is wind speed

I am arranging my data for 1 day ahead prediction like:

d = ones(1,365);

X_new = [Temperature; humidity; pressure];

X = mat2cell (X_new, 3, 24*d);

Input series = X

T_new = [wind speed];

T = mat2cell (T_new,1, 24*d);

Target series = T

For next step i want to calculate p, d, q Can any one help me to write code for next step about my input series and target series

Answer 1

[Hassaan]

% Assuming X_new and T_new are your full datasets for temperature, humidity,
% pressure, and wind speed respectively

% Reshape the data into a 365x24 matrix (assuming hourly data)
Temperature = reshape(Temperature, 24, []); % Reshape into 24-hour segments
Humidity = reshape(Humidity, 24, []);
Pressure = reshape(Pressure, 24, []);
WindSpeed = reshape(WindSpeed, 24, []);

% Prepare the dataset for ARIMA; usually ARIMA works with univariate series,
% so you may need to run separate models for each input or create a multivariate model.
% Here's how you would prepare for a univariate ARIMA for wind speed prediction.

% For simplicity, let's predict wind speed based on its past values.
% Flatten the data back to a vector for ARIMA
WindSpeedVector = WindSpeed(:);

% Divide the data into training and testing
trainData = WindSpeedVector(1:end-24); % all data except the last day
testData = WindSpeedVector(end-23:end); % the last day for validation

% Use autocorrelation to determine a good starting point for 'p' and 'q'
figure;
autocorr(trainData);
figure;
parcorr(trainData);

% Use ADF test to determine if data is stationary or how much differencing 'd' is needed
[~,pValue,~,~,~] = adftest(trainData);

% If pValue is greater than 0.05, we may need to difference the data
if pValue > 0.05
    d = 1;
else
    d = 0;
end

% Initialize a range of p and q to try
p = 0:3;
q = 0:3;

% Initialize AIC, BIC matrices to store the values for each model
AIC = zeros(length(p), length(q));
BIC = zeros(length(p), length(q));

% Grid search over p and q values
for i = 1:length(p)
    for j = 1:length(q)
        model = arima(p(i), d, q(j));
        try
            [fit,~,logL] = estimate(model, trainData, 'Display', 'off');
            [aic, bic] = aicbic(logL, numParams(fit), length(trainData));
            AIC(i,j) = aic;
            BIC(i,j) = bic;
        catch e
            % If model fails to converge, assign a large AIC/BIC
            AIC(i,j) = inf;
            BIC(i,j) = inf;
        end
    end
end

% Find the indices of the minimum AIC and BIC
[minAIC, idxAIC] = min(AIC(:));
[minBIC, idxBIC] = min(BIC(:));

% Convert linear indices to subscripts to find corresponding p and q values
[pAIC, qAIC] = ind2sub(size(AIC), idxAIC);
[pBIC, qBIC] = ind2sub(size(BIC), idxBIC);

% Display the results
fprintf('Optimal p and q according to AIC: p = %d, q = %d\n', pAIC, qAIC);
fprintf('Optimal p and q according to BIC: p = %d, q = %d\n', pBIC, qBIC);

A basic grid search approach for determining the ARIMA model's p and q parameters based on AIC and BIC, and d based on the ADF test. The autocorrelation and partial autocorrelation plots can provide insights into potential values for p and q. You might need to adjust the code according to your specific data structure and requirements.

------------------------------------------------------------------------------------------------------------------------------------------------

If you find the solution helpful and it resolves your issue, it would be greatly appreciated if you could accept the answer. Also, leaving an upvote and a comment are also wonderful ways to provide feedback.

Professional Interests

• Technical Services and Consulting
• Embedded Systems | Firmware Developement | Simulations
• Electrical and Electronics Engineering

Feel free to contact me.

[Seemant Tiwari]

Plz share your mail id.

Thank you very much

[Seemant Tiwari]

Hi,

I want to create model. now i know p, d, q value. can you tell me, how can we get these values?

MD = ARIMA ('AR', { }, 'MA', { }, 'SAR', { }, 'SMA', { }, 'D', .., 'SEASONALITY',.., 'CONSTANT',.., 'VARIANCE'..)

How shall we calculate value of SAR, SMA, CONSTANT AND VARIANCE.?

For AR value is 1

MA value is 1

D is 1