% time1383 and data1383 available for use
% for Total likes of Teja's Webcam Sudoku Solver video on YouTube
% 
% Total views of Teja's Webcam Sudoku Solver video on YouTube
%   time vector is: time1382
%   data vector is: data1382

subplot(2,1,1)

  % Fit the growth of total views
  [p,S,mu] = polyfit(time1382,data1382,1);
  p1 = [p(1)/mu(2) p(2)-p(1)*mu(1)/mu(2)];

  % Project into the future
  tf = [time1382; time1382(end)+1];
  df = polyval(p1,tf);

  % Predict the number for the next day
  d=round(df(end));
  t=datestr(time1382(end)+1,1);
  s=sprintf('Prediction for %s: %d followers',t,d);

  % Predict when it will reach a goal
  goal = 8000;
  g = sprintf('Will reach %d views on ', goal);
  g = [g datestr((goal - p1(2))/p1(1),1)];

  % Was previous prediction correct?
  [p,S,mu] = polyfit(time1382(1:end-1),data1382(1:end-1),1);
  p2 = [p(1)/mu(2) p(2)-p(1)*mu(1)/mu(2)];
  prev = polyval(p2,time1382(end-1)+1);
  prev = round(prev);
  gap = prev-data1382(end);
  t=datestr(time1382(end),1);
  p=sprintf('Prev Prediction for %s off by %d',t,gap);

  % Plot the data and trend
  plot(time1382,data1382, '-');
  hold on
  line(tf,df,'Color','magenta','LineStyle','-')
  plot(time1382(end),prev, 'ro');
  datetick
  title('Total views of Teja''s Sudoku video')
  legend('Total Views','Trend','Prev Prediction', 'Location', 'Northwest')
  xlabel(s)
  hold off
 
  % Get normalized position for annotation
  xrange = xlim;
  xmin = xrange(1);
  xmax = xrange(2);
  yrange = ylim;
  ymin = yrange(1);
  ymax = yrange(2);

  % Add the prediction
  XPos = xmin+0.35*(xmax-xmin);
  YPos1 = ymin+0.2*(ymax-ymin);
  YPos2 = ymin+0.1*(ymax-ymin);
  text(XPos,YPos1,p)
  text(XPos,YPos2,g)

subplot(2,1,2)
  % Adjust the Y axis
  ymin = fix(min(data1383));
  ymax = ceil(max(data1383));

  plot(time1383,data1383, '-');
  datetick
  title('Total likes of Teja''s Sudoku video')
  ylim([ymin-1 ymax+1])
  set(gca,'YTick',ymin-1:ymax+1)