Forecast(model) from Forecast package, it returns point forecast along with upper and lower forecast intervals. Is there a way to extract the exact distribution for each forecast value so that I can make a histogram for every row of forecast? Having the intervals are't sufficient enough to make histograms shown below.
> forecast(mod,12)
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
12 0.000284821 0.0002356356 0.0003340064 2.095985e-04 0.0003600435
13 0.000284821 0.0002237453 0.0003458967 1.914137e-04 0.0003782283
14 0.000284821 0.0002138190 0.0003558230 1.762328e-04 0.0003934092
15 0.000284821 0.0002051195 0.0003645225 1.629281e-04 0.0004067140
16 0.000284821 0.0001972803 0.0003723617 1.509390e-04 0.0004187030
17 0.000284821 0.0001900876 0.0003795544 1.399388e-04 0.0004297033
18 0.000284821 0.0001834037 0.0003862383 1.297167e-04 0.0004399253
19 0.000284821 0.0001771339 0.0003925081 1.201278e-04 0.0004495142
20 0.000284821 0.0001712096 0.0003984324 1.110674e-04 0.0004585746
21 0.000284821 0.0001655793 0.0004040627 1.024565e-04 0.0004671855
22 0.000284821 0.0001602030 0.0004094390 9.423428e-05 0.0004754077
23 0.000284821 0.0001550494 0.0004145927 8.635240e-05 0.0004832896
The distribution is normal for all ARIMA models provided the residuals are normally distributed. So you can easily obtain the mean and variance of all future time periods from the point forecast and upper/lower bounds.
If you are unwilling to assume the residuals are normal, you can simulate the future distributions as follows.
library(forecast)
nsim <- 1000
mod <- auto.arima(WWWusage)
sim <- matrix(NA, nrow=9, ncol=nsim)
for(i in 1:nsim)
sim[,i] <- simulate(mod, nsim=9, future=TRUE, bootstrap=TRUE)
par(mfrow=c(3,3))
for(i in 1:9)
hist(sim[i,], breaks="FD", main=paste("h=",i))
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I have a plotted spectral curve in google sheets. All points are real coordinates. As you can see, data is not provided for the slope below 614nm. I would like to extend the slope beyond the supplied data, so that it reaches 0. In a mathematically relevant way to follow the trajectory it was taking from when the slope started. Someone mentioned to me I would have to potentially use a linear regression? I'm not sure what that is. How would I go about extending this slope relevant to it's defined trajectory down to 0 in google sheets?
Here's the data
x-axis:
614
616
618
620
622
624
626
628
630
632
634
636
638
640
642
644
646
648
650
652
654
656
658
660
662
664
666
668
670
672
674
676
678
680
682
684
686
688
690
692
694
696
698
700
702
704
706
708
710
712
714
716
718
720
722
724
726
728
730
y-axis:
0.7101
0.7863
0.8623
0.9345
1.0029
1.069
1.1317
1.1898
1.2424
1.289
1.3303
1.3667
1.3985
1.4261
1.4499
1.47
1.4867
1.5005
1.5118
1.5206
1.5273
1.532
1.5348
1.5359
1.5355
1.5336
1.5305
1.5263
1.5212
1.5151
1.5079
1.4994
1.4892
1.4771
1.4631
1.448
1.4332
1.4197
1.4088
1.4015
1.3965
1.3926
1.388
1.3813
1.3714
1.359
1.345
1.3305
1.3163
1.303
1.2904
1.2781
1.2656
1.2526
1.2387
1.2242
1.2091
1.1937
1.1782
Thanks
I understand that you want The curve should be increased beyond the given data in a mathematically sound fashion until it approaches 0, In what follows, I'm going to show how it's done with the last 2 data points which make the filled data linear it might help, like this: take a look at this Sheet.
We need to
1 - Paste this SEQUENCE function formula in C3 to get the order of input
=SEQUENCE(COUNTA(B3:B),1,1,1)
2 - SORT the the input by pasting this formula in E3.
=SORT(A3:C61,3,0)
3 - In F62 after the last line of the sorted data paste this TREND function that Fits an ideal linear trend using the least squares approach to incomplete data about a linear trend and/or makes additional value predictions..
=TREND(F60:F61,E60:E61,E62:E101)
TREND takes
'known_data_y' set to F60:F61
'[known_data_x]' set to E61,E62 those are the 2 data point
[known_data_x] set to E62:E101, we get it by pasting E62:E101 after the last line of the sorted data in "x-axis:" in output table cell E62
4 - To see the newly genrated data in the red curve we need a new column that start from K62 till the very bottom of the data "y-axis:" in output table cell K62, by pasting this ArrayFormula in K62.
=ArrayFormula(E62:G101)
5 - Add a Serie in tne chart in chart editor > setup > Series > Add Serie.
I am using LSTM to forecast the energy demand of the next 24 hours of a household. I am using x1 = temperature and x2 = precipitation and y = energy consumption.
If I use 1 year of historical data (hour by hour) and I want to calculate the next 24 steps, how can I include the temperature prediction and precipitation prediction data for the next 24 steps?
In other words, how can include the values of the variables x1 and x2 for the time steps that I am looking to forecast for y?
time X1 X2 Y
t-999 22 33 32
... ... ... ...
t-3 23 44 21
t-2 25 44 33
t-1 22 55 42
t 21 22 22
t+1 24 22 ?
t+2 22 13 ?
... ... ... ...
t+24 24 32 ?
Thanks!
I have problem with GridSearchCV freezing (CPU is active but program in not advancing) with linear svm (but with rbf svm it works fine).
Depending on the random_state that I use for splitting my data, I have this freezing in different splits points of cv for different PCA components?
The features of one sample looks like the following(it is about 39 features)
[1 117 137 2 80 16 2 39 228 88 5 6 0 10 13 6 22 23 1 227 246 7 1.656934307 0 5 0.434195726 0.010123735 0.55568054 5 275 119.48398 0.9359527 0.80484825 3.1272728 98 334 526 0.13454546 0.10181818]
Another sample's features:
[23149 4 31839 9 219 117 23 5 31897 12389 108 2 0 33 23 0 0 18 0 0 0 23149 0 0 74 0.996405221 0.003549844 4.49347E-05 74 5144 6.4480677 0.286384 0.9947901 3.833787 20 5135 14586 0.0060264384 0.011664075]
If I delete the last 10 feature I don't have this problem ( The 10 new features that I added before my code worked fine). I did not check other combinations of the 10 last new features to check if a specific feature is causing this problem.
Also I use StandardScaler to scale the features but still facing this issue. I have less of this problem if I use MinMaxScaler scaler (but read soewhere it is not good for svm).
I also put n_jobs to different numbers and it only could advance by little but freezes again.
What do you suggest?
I followed part of this code to write my code:
TypeError grid seach
I'm newbie in machine learning, so I need your advice.
Imagine, we have two data sets (df1 and df2).
First data set include about 5000 observations and some features, to simplify:
name age company degree_of_skill average_working_time alma_mater
1 John 39 A 89 38 Harvard
2 Steve 35 B 56 46 UCB
3 Ivan 27 C 88 42 MIT
4 Jack 26 A 87 37 MIT
5 Oliver 23 B 76 36 MIT
6 Daniel 45 C 79 39 Harvard
7 James 34 A 60 40 MIT
8 Thomas 28 B 89 39 Stanford
9 Charlie 29 C 83 43 Oxford
The learning problem - to predict productivity of companies from second data set (df2) for next period of time (june-2016), based on data from the first data set (df1).
df2:
company productivity date
1 A 1240 april-2016
2 B 1389 april-2016
3 C 1388 april-2016
4 A 1350 may-2016
5 B 1647 may-2016
6 C 1272 may-2016
So as we can see both data sets include feature "company". But I don't understand how I can create a link between these two features. What shoud I do with two data sets to solve the learning problem? Is it possible?
It is known that if we are finding the most matching window to the current window in the entire image, then wherever the correlation is maximised then that is the matching window.
[22 12 14] (window)
(image)
[22 12 34 54 ]
[112 34 54 111 ]
[12 22 12 34 ]
[11 22 12 14 ]
But correlation is product of corresponding values in two windows.
So, if some of the window have high intensity values then they will always provide better match. e.g. in above example we will have higher correlation value for 2nd row.
Probably you need Normalized Cross Correlation, the maximum will be in 4th row.