MATLAB: One Step Ahead Neural Network Timeseries Forecast

lag, matlab, neural-network, prediction, time-series

Intro: I'm using MATLAB's Neural Network Toolbox in an attempt to forecast time series one step into the future. Currently I'm just trying to forecast a simple sinusoidal function, but hopefully I will be able to move on to something a bit more complex after I obtain satisfactory results.

Problem: Everything seems to work fine, however the predicted forecast tends to be lagged by one period. Neural network forecasting isn't much use if it just outputs the series delayed by one unit of time, right?


t = -50:0.2:100;noise = rand(1,length(t));y = sin(t)+1/2*sin(t+pi/3);split = floor(0.9*length(t));forperiod = length(t)-split;numinputs = 5;forecasted = [];msg = '';for j = 1:forperiod    fprintf(repmat('\b',1,numel(msg)));    msg = sprintf('forecasting iteration %g/%g...\n',j,forperiod);    fprintf('%s',msg);    estdata = y(1:split+j-1);    estdatalen = size(estdata,2);    signal = estdata;    last = signal(end);    [signal,low,high] = preprocess(signal'); % pre-process    signal = signal';    inputs = signal(rowshiftmat(length(signal),numinputs));    targets = signal(numinputs+1:end);    %% NARNET METHOD    feedbackDelays = 1:4;    hiddenLayerSize = 10;    net = narnet(feedbackDelays,[hiddenLayerSize hiddenLayerSize]);    net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};    signalcells = mat2cell(signal,[1],ones(1,length(signal)));    [inputs,inputStates,layerStates,targets] = preparets(net,{},{},signalcells);    net.trainParam.showWindow = false;    net.trainparam.showCommandLine = false;    net.trainFcn = 'trainlm';  % Levenberg-Marquardt    net.performFcn = 'mse';  % Mean squared error    [net,tr] = train(net,inputs,targets,inputStates,layerStates);    next = net(inputs(end),inputStates,layerStates);    next = postprocess(next{1}, low, high); % post-process    next = (next+1)*last;    forecasted = [forecasted next];endfigure(1);plot(1:forperiod, forecasted, 'b', 1:forperiod, y(end-forperiod+1:end), 'r');grid on;

The function 'preprocess' simply converts the data into logged % differences and 'postprocess' converts the logged % differences back for plotting. (Check EDIT for preprocess and postprocess code)


A screenshot of the forecasting results using MATLAB.

BLUE: Forecasted Values

RED: Actual Values

Can anyone tell me what I'm doing wrong here? Or perhaps recommend another method to achieve the desired results (lagless prediction of sinusoidal function, and eventually more chaotic timeseries)? Your help is very much appreciated.

It's been a few days now and I hope everyone has enjoyed their weekend. Since no solutions have emerged I've decided to post the code for the helper functions 'postprocess.m', 'preprocess.m', and their helper function 'normalize.m'. Maybe this will help get the ball rollin.


function data = postprocess(x, low, high)% denormalizelogdata = (x+1)/2*(high-low)+low;% inverse log datasign = logdata./abs(logdata);data = sign.*(exp(abs(logdata))-1);end


function [y, low, high] = preprocess(x)% differencingdiffs = diff(x);% calc % changeschngs = diffs./x(1:end-1,:);% log datasign = chngs./abs(chngs);logdata = sign.*log(abs(chngs)+1);% normalize logretshigh = max(max(logdata));low = min(min(logdata));y=[];for i = 1:size(logdata,2)    y = [y normalize(logdata(:,i), -1, 1)];endend


function Y = normalize(X,low,high)%NORMALIZE Linear normalization of X between low and high values.if length(X) <= 1    error('Length of X input vector must be greater than 1.');endmi = min(X);ma = max(X);Y = (X-mi)/(ma-mi)*(high-low)+low;end

Best Solution

I didn't check you code, but made a similar test to predict sin() with NN. The result seems reasonable, without a lag. I think, your bug is somewhere in synchronization of predicted values with actual values.Here is the code:

%% init & paramst = (-50 : 0.2 : 100)';y = sin(t) + 0.5 * sin(t + pi / 3);sigma = 0.2;n_lags = 12;hidden_layer_size = 15;%% create netnet = fitnet(hidden_layer_size);%% trainnoise = sigma * randn(size(t));y_train = y + noise;out = circshift(y_train, -1);out(end) = nan;in = lagged_input(y_train, n_lags);net = train(net, in', out');%% testnoise = sigma * randn(size(t)); % new noisey_test = y + noise;in_test = lagged_input(y_test, n_lags);out_test = net(in_test')';y_test_predicted = circshift(out_test, 1); % sync with actual valuey_test_predicted(1) = nan;%% plotfigure, plot(t, [y, y_test, y_test_predicted], 'linewidth', 2); grid minor; legend('orig', 'noised', 'predicted')

and the lagged_input() function:

function in = lagged_input(in, n_lags)    for k = 2 : n_lags        in = cat(2, in, circshift(in(:, end), 1));        in(1, k) = nan;    endend

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