I have a question,
In Matlab, I have a vector of 20 years of daily data (X) and a vector of the relevant dates (DATES). In order to find the mean value of the daily data per year, I use the following script:
A = fints(DATES,X); %convert to financial time series
B = toannual(A,'CalcMethod', 'SimpAvg'); %calculate average value per year
C = fts2mat(B); %Convert fts object to vector
C is a vector of 20 values. showing the average value of the daily data for each of the 20 years. So far, so good.. Now I am trying to do the same thing but instead of calculating mean values annually, i need to calculate std annually but it seems there is not such an option with function "toannual".
Any ideas on how to do this?
THANK YOU IN ADVANCE
I'm assuming that X is the financial information and it is an even distribution across each year. You'll have to modify this if that isn't the case. Just to clarify, by even distribution, I mean that if there are 20 years and X has 200 values, each year has 10 values to it.
You should be able to do something like this:
num_years = length(C);
span_size = length(X)/num_years;
for n = 0:num_years-1
std_dev(n+1,1) = std(X(1+(n*span_size):(n+1)*span_size));
end
The idea is that you simply pass the date for the given year (the day to day values) into matlab's standard deviation function. That will return the std-dev for that year. std_dev should be a column vector that correlates 1:1 with your C vector of yearly averages.
unique_Dates = unique(DATES) %This should return a vector of 20 elements since you have 20 years.
std_dev = zeros(size(unique_Dates)); %Just pre allocating the standard deviation vector.
for n = 1:length(unique_Dates)
std_dev(n) = std(X(DATES==unique_Dates(n)));
end
Now this is assuming that your DATES matrix is passable to the unique function and that it will return the expected list of dates. If you have the dates in a numeric form I know this will work, I'm just concerned about the dates being in a string form.
In the event they are in a string form you can look at using regexp to parse the information and replace matching dates with a numeric identifier and use the above code. Or you can take the basic theory behind this and adapt it to what works best for you!
Related
Given a dataset of blood results, say cholesterol level, and knowing that the instrument that produced those results is subject to a known degree of variability, how would I add that variability back into the dataset? i.e. I want to assume the result in the original dataset is the true/mean value, and then produce new results that are subject to the known variability of the instrument.
In Excel you use =NORM.INV(RAND(), mean, std_dev), where RAND() provides a random value between 0 and 1, "mean" will be the original value and I have the CV so I can calculate the SD. NORM.INV then provides the inverse of the cumulative normal distribution function.
I've done the following to create a new column with my new values, but would like to know if it is valid (i.e., will each row have a different random number between 0 and 1 as the probability? and is this formula equivalent to NORM.INV?
df8000['HDL_1'] = norm.ppf(random(), loc = df8000['HDL_0'], scale = TAE_df.loc[0,'HDL'])
Thanks in advance!
I'm trying to calculate the MAX value of column D of value E which resets for every A Column. Later I just need to show the MAX row of A.
Raw data looks like this:
The desired view would be like this:
Thanks!
This is an excellent example of a question which can/should be answered using LOD Calculations.
To start, find the maximum [Percentage] (Column E) per [Name] (Column B):
{Fixed [Name]: MAX(Percentage)}
Then insert that calculation, call it "Max Per Name", into another calculation which will only show the [Letter] (Column D) if the [Percentage] and the [Max Per Name] match:
IF [Percentage] = [Max Per Name] THEN [Letter] END
The result will be a dimension which shows the [Letter] which matches the largest [Percentage] and 'Null' when not matched.
From here, just filter out 'Null' - the result will be exactly as you desire. You will even be able to take [Percentage] off the view and still see an accurate [Letter].
Please note that while LOD Calculations are extremely powerful, they require a little more work to maintain and operate. They are a little more complex than normal calculations. Please take some time and read through this LOD Overview document to further familiarize yourself with their use/limitations.
In a fairly balanced binomial classification response problem, I am observing unusual level of error in h2o.gbm classification for determining class 0, on train set itself. It is from a competition which is over, so interest is only towards understanding what is going wrong.
Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
0 1 Error Rate
0 147857 234035 0.612830 =234035/381892
1 44782 271661 0.141517 =44782/316443
Totals 192639 505696 0.399260 =278817/698335
Any expert suggestions to treat the data and reduce the error is welcome.
Following approaches are tried and error is not found decreasing.
Approach 1: Selecting top 5 important variables via h2o.varimp(gbm)
Approach 2: Converting the negative normalized variable as zero and possitive as 1.
#Data Definition
# Variable Definition
#Independent Variables
# ID Unique ID for each observation
# Timestamp Unique value representing one day
# Stock_ID Unique ID representing one stock
# Volume Normalized values of volume traded of given stock ID on that timestamp
# Three_Day_Moving_Average Normalized values of three days moving average of Closing price for given stock ID (Including Current day)
# Five_Day_Moving_Average Normalized values of five days moving average of Closing price for given stock ID (Including Current day)
# Ten_Day_Moving_Average Normalized values of ten days moving average of Closing price for given stock ID (Including Current day)
# Twenty_Day_Moving_Average Normalized values of twenty days moving average of Closing price for given stock ID (Including Current day)
# True_Range Normalized values of true range for given stock ID
# Average_True_Range Normalized values of average true range for given stock ID
# Positive_Directional_Movement Normalized values of positive directional movement for given stock ID
# Negative_Directional_Movement Normalized values of negative directional movement for given stock ID
#Dependent Response Variable
# Outcome Binary outcome variable representing whether price for one particular stock at the tomorrow’s market close is higher(1) or lower(0) compared to the price at today’s market close
temp <- tempfile()
download.file('https://github.com/meethariprasad/trikaal/raw/master/Competetions/AnalyticsVidhya/Stock_Closure/test_6lvBXoI.zip',temp)
test <- read.csv(unz(temp, "test.csv"))
unlink(temp)
temp <- tempfile()
download.file('https://github.com/meethariprasad/trikaal/raw/master/Competetions/AnalyticsVidhya/Stock_Closure/train_xup5Mf8.zip',temp)
#Please wait for 60 Mb file to load.
train <- read.csv(unz(temp, "train.csv"))
unlink(temp)
summary(train)
#We don't want the ID
train<-train[,2:ncol(train)]
# Preserving Test ID if needed
ID<-test$ID
#Remove ID from test
test<-test[,2:ncol(test)]
#Create Empty Response SalePrice
test$Outcome<-NA
#Original
combi.imp<-rbind(train,test)
rm(train,test)
summary(combi.imp)
#Creating Factor Variable
combi.imp$Outcome<-as.factor(combi.imp$Outcome)
combi.imp$Stock_ID<-as.factor(combi.imp$Stock_ID)
combi.imp$timestamp<-as.factor(combi.imp$timestamp)
summary(combi.imp)
#Brute Force NA treatment by taking only complete cases without NA.
train.complete<-combi.imp[1:702739,]
train.complete<-train.complete[complete.cases(train.complete),]
test.complete<-combi.imp[702740:804685,]
library(h2o)
y<-c("Outcome")
features=names(train.complete)[!names(train.complete) %in% c("Outcome")]
h2o.shutdown(prompt=F)
#Adjust memory size based on your system.
h2o.init(nthreads = -1,max_mem_size = "5g")
train.hex<-as.h2o(train.complete)
test.hex<-as.h2o(test.complete[,features])
#Models
gbmF_model_1 = h2o.gbm( x=features,
y = y,
training_frame =train.hex,
seed=1234
)
h2o.performance(gbmF_model_1)
You've only trained a single GBM with the default parameters, so it doesn't look like you've put enough effort into tuning your model. I'd recommend a random grid search on GBM using the h2o.grid() function. Here is an H2O R code example you can follow.
I have following problem:
I need to write a begin and end date into a matrix. Where the matrix contains the yearly quarters (1-4) in the collumns and the rows are the year.
E.g.
Matrix:
Q1 Q2 Q3 Q4
2010
2011
Now the Date 01.01.2010 should be put in the first element and the date 09.20.2011 in the sixed element.
Thanks in advance.
You first have to consider that SAS does not actually have date/time/datetime variables. It just uses numeric variables formatted as date/time/datetime. The actual value being:
days since 1/1/1960 for dates
seconds since 00:00 for times
seconds since 1/1/1960 00:00 for datetimes
SAS does not even distinguish between integer and float numeric types. So a date value can contain a fractional part.
What you do or can do with a SAS numeric variable is completely up to you, and mostly depends on the format you apply. You could mistakenly format a variable containing a date value with a datetime format... or even with a currency format... SAS won't notice or complain.
You also have to consider that SAS does not even actually have matrixes and arrays. It does provide a way to simulate their use to read and write to dataset variables.
That said, SAS does provide a whole lot of formats and informats that allow you to implement date and time manipulation.
Assuming you are coding within a data step, and assuming the "dates" are in dataset numeric variables, then the PUT function can extract the datepart you need to calculate row, column of the matrix element to write to, like so:
DATA table;
ARRAY dm{2,4} dm_r1c1-dm_r1c4 dm_r2c1-dm_r2c4;
beg_row = PUT(beg_date, YEAR4.)-2009;
end_row = PUT(end_date, YEAR4.)-2009;
beg_col = PUT(beg_date, QTR1.);
end_col = PUT(end_date, QTR1.);
dm{beg_row,beg_col} = beg_date;
dm{end_row,end_col} = end_date;
RUN;
... or if you are using a one-dimensional array:
DATA table;
ARRAY da{8} da_1-da_8;
beg_index = 4 * (PUT(beg_date, YEAR4.)-2010) + PUT(beg_date, QTR1.);
end_index = 4 * (PUT(end_date, YEAR4.)-2010) + PUT(end_date, QTR1.);
da{beg_index} = beg_date;
da{end_index} = end_date;
RUN;
I have 2 independent but contiguous date ranges. The first range is the start and end date for a project. Lets say start = 3/21/10 and end = 5/16/10. The second range is a month boundary (say 3/1/10 to 3/31/10, 4/1/10 to 4/30/10, etc.) I need to figure out how many days in each month fall into the first range.
The answer to my example above is March = 10, April = 30, May = 16.
I am trying to figure out an excel formula or VBA function that will give me this value.
Any thoughts on an algorithm for this? I feel it should be rather easy but I can't seem to figure it out.
I have a formula which will return TRUE/FALSE if ANY part of the month range is within the project start/end but not the number of days. That function is below.
return month_start <= project_end And month_end >= project_start
Think it figured it out.
=MAX( MIN(project_end, month_end) - MAX(project_start,month_start) + 1 , 0 )