# The following will delete the TIF file that was created by CopyRaster tool.Īrcpy.Delete_management(Output_Raster,"#")Īrcpy. Output_Band = Output_Folder + os.sep + "Band_" + str(count) +".tif"Īrcpy.CopyRaster_management(Input_Raster_Name, Output_Band)Īrcpy.AddMessage("Band_" + str(count) +".tif" + " " "exported" + " " + "successfully") timeslice function - RDocumentation bibliometrix (version 3.2.1) timeslice: Bibliographic data frame time slice Description Divide a bibliographic data frame into time slice Usage timeslice (M, breaks NA, k 5) Arguments M is a bibliographic data frame obtained by the converting function convert2df. Input_Raster_Name = Output_Raster + os.sep+ "Band_" + str(count) #Loop through the bands and copy bands as a seperate TIF file. #Reading number of band information from saved TIFīandcount = arcpy.GetRasterProperties_management (Output_Raster, "BANDCOUNT")Īrcpy.AddMessage("Exporting individual bands from" + Output_Raster) Output_Raster = Output_Folder + os.sep + "NetCDF_Raster.tif"Īrcpy.CopyRaster_management(Input_Name, Output_Raster)Īrcpy.AddMessage(Output_Raster + " " + "created from NetCDF layer") You first need to convert the slice you want to export into a raster object in R (defined in the raster package): r <- as. Output_Folder = arcpy.GetParameterAsText(1) of time-slice activity recognition which aims to explore human activity at a smaller tem. Another person contacted me recently about this and here is the example: library (caret) library (ggplot2) data (economics) myTimeControl <- trainControl (method 'timeslice', initialWindow 36, horizon 12, fixedWindow TRUE) plsFitTime. Input_NetCDF_layer = arcpy.GetParameterAsText(0) The documentation is being 'improved' on this feature (in other words, it currently sucks). This code can be modified to meet various needs. My question: How is it possible that R squared and the general fit becomes worse with more variables fed to the model? My intuition is that the model should be at least as good as the previous model, given that all 13 variables from that model are also available for this model.The tool exports all the time slices (bands) from the NetCDF raster layer as TIFs. The following shows the Python code used for this script tool. Unfortunately, I cannot post my data due to policy restrictions. I would like to understand how to go about selecting values for 'initialWindow', 'horizon' and 'fixedWindow' in trainControl. Referring to this post: createTimeSlices function in CARET package in R where createTimeSlices was suggested as an option for cross-validating when using time series data. The model with the worse fit has a set of 35 independent variables which includes all 13 variables from the previous model, a lambda of 41 and an alpha of 1 (lasso regression).įor model fitting I test lambda values from 0 to 300 and alpha values from 0 to 1 in 0.1 steps. Time Series - Splitting Data Using The timeSlice Method. The previously fitted model only included a set of 13 independent variables, lambda was 0 (so basically just a linear regression). However, when fitting a model with 35 candidate variables, the model fit suddenly becomes noticeably worse in terms of both RMSE and R squared (and not only marginally: RMSE from 2000 to 2500, R squared from 0.88 to 0.85). The problem that I face is: I am iteratively fitting multiple models, for each model I add another block of variables to see whether this block of variables can improve the model. I am fitting the model on 45 data points and use timeslice for validation (I know the number of iterations should be higher but there is simply no more data). Timeslice also supports bucketing by a fixed number of buckets across the search results, for example, 150 buckets over the last 60 minutes. Metric="RMSE", tuneGrid=id(alpha=alpha, lambda=lambda) The timeslice operator segregates data by time period, so you can create bucketed results based on a fixed width in time, for example, five minute periods. But the idea is to see how well your models predict using data the model has not seen before. More sophisticated methods like cross validation use multiple holdout samples. Then internally validate your models using the holdout sample. Pretty straight forward to change this suit your number of variables or time dimension etc. Set aside a portion of your data (say, 30). TrControl = trainControl("timeslice", initialWindow = 30, horizon = 3, fixedWindow = FALSE), I know this is an old question, but if anyone is still looking for an answer on how to do this in R the following code that will make a netCDF with 2 variables (prec, tmax), and 3 dimensions (long, lat, time). Mele & Piers Rawling - 2004 - In Alfred R. I am using glmnet and caret to iteratively fit forecast models in a time series context. I advocate Time-Slice Rationality, the thesis that the relationship.
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