Required Libraries

Load required libraries and plot template.

#Load Required Libraries
library(solarf)
library(knitr) #For pretty tables on the report using kable 
library(plyr)
library(ggplot2)
library(GGally)
library(caret)
library(extrafont)
#Theme for Plot
science_theme = 

theme(
    panel.background=element_blank(), 
    #panel.grid.major = element_line(size = 0.5, color = "grey"),
    #axis.line = element_line(size = 0.7, color = "black"), 
    #legend.position = c(0.9, 0.7), 
    #text = element_text(size = 14),
    axis.line=element_blank(),
    text = element_text(size=15, family="Interstate"),
    axis.text.x = element_text(hjust = 1, size=15,color="black"), 
    axis.text.y = element_text(hjust = 1, size=15,color="black"), 
    axis.title.y=element_text(size=15), 
    axis.title.x=element_text(size=15),
    axis.ticks = element_line(colour=NA),
    panel.grid.major = element_line(colour = 'gray', linetype = 'dashed'),
    panel.grid.minor = element_line(colour = NA),  
    panel.background = element_rect(colour = 'white'))

Data Preparation Parameters

variable <- "SOLR"
nclusters <- 5

SOLR

SCBH1 <- readRDS("/Users/carlos/Google Drive/Research/SolarForecasting/datasets/mesowest/data/SCBH1.rds")

Vectorization

Vectorize SCBH1 over the training and test partitions.

vector <- station.vectorize(SCBH1,variable)
vector <- vector[,c(1:3,12:21)]

Cleansing

If SOLR is missing any hour value, the day is discarded.

vector <- vector[complete.cases(vector),]

Discretization 5-means

Cluster with 5-means the dataset to obtain the daily profiles.

#Set seed for reproducibility
set.seed(1234)
kmeans.results <- kmeans(vector[,4:ncol(vector)],nclusters)
vector$clusterid <- kmeans.results[[1]]
centroids <- kmeans.results[[2]]

#Order the centroids in crescent order of intensity and reset rows
ordered.centroids <- as.numeric(names(sort(rowSums(centroids))))
centroids <- centroids[ordered.centroids,]
rownames(centroids) <- seq(1,nclusters)

#Update the centroid ids, by mapping the row ids before ordering to after ordering (element position is old ordering, value is new ordering)
vector$clusterid <- ordered.centroids[vector$clusterid]

Transitions

Define the istransition variable by observing if the previous day had a different clusterid than the current.

vector$istransition <- vector$clusterid
for (i in 2:length(vector$istransition)){
    if(vector$istransition[i-1] == vector$istransition[i])
        vector$istransition[i] <- 0 #False for C++
    else
        vector$istransition[i] <- 1 #True for C++
}
vector$istransition[1] <- -9999 # NA for C++
vector.solar <- vector

Summary

Sample 5 vectors.

kable(vector[sample(nrow(vector),5),])

	YEAR	MON	DAY	8	9	10	11	12	13	14	15	16	17	clusterid	istransition
2853	2011	8	20	64	140	134	149	201	188	146	211	129	81	4	1
38	2003	2	7	372	620	736	826	850	793	685	497	280	60	2	1
959	2006	3	24	199	529	929	995	726	501	333	218	181	59	2	1
2939	2011	11	15	113	146	185	207	250	264	149	227	73	7	4	1
1932	2008	12	18	62	116	296	304	260	565	472	449	134	16	4	1

Centroids.

centroids_plot <- data.frame(centroids)
centroids_plot$color <-rownames(centroids)
colnames(centroids_plot) <- c(colnames(centroids),"Profile")
kable(centroids)

8	9	10	11	12	13	14	15	16	17
96.48299	147.0476	185.7497	209.1020	225.3701	235.7184	240.3211	238.3252	202.2259	109.97551
265.63504	416.2701	524.8015	536.3723	466.2657	392.3810	337.9927	257.6292	170.9825	70.75328
179.27831	312.7568	434.9427	547.3954	651.0064	687.7654	639.9725	530.1320	341.7138	146.76885
359.17192	574.7114	732.5726	803.5174	768.3454	669.8628	540.4795	396.4653	258.4795	108.48107
357.29153	573.5679	752.0663	885.0358	954.2930	941.3826	841.5112	675.5182	450.1414	200.40016

ggparcoord(
  data = centroids_plot,
  columns=c(1:(ncol(centroids)-1)),
  scale="globalminmax",
  alphaLines=0.2,
  groupColumn=ncol(centroids_plot)
) + geom_line(size=2,alpha=0.6) + science_theme + xlab("Hour of the Day") +
  ylab("Solar Radiation Itensity (W/m2") + ggtitle(paste('SCBH1',variable,'Centroids'))# + theme(axis.text.x = element_text(hjust = 1, size=20,color="black"), axis.text.y = element_text(hjust = 1, size=20,color="black"), plot.title = element_text(size=20), axis.title.y=element_text(size=20), axis.title.x=element_text(size=20))

Data Preparation Parameters

variable <- "PREC"
nclusters <- 5

PREC

Vectorization

Vectorize SCBH1 over the training and test partitions.

vector <- station.vectorize(SCBH1,variable)
vector <- vector[,c(1:3,12:21)]

Cleansing

If PREC is missing any hour value, the day is discarded.

vector <- vector[complete.cases(vector),]

Discretization Binning (since Centroids are constant lines)

#Set seed for reproducibility
set.seed(1234)

#Mean precipitation of each day
mean.prec <- rowMeans(vector[,4:ncol(vector)])

library(Hmisc)

## Warning: package 'Hmisc' was built under R version 3.1.3

## Loading required package: grid
## Loading required package: survival

## Warning: package 'survival' was built under R version 3.1.3

## 
## Attaching package: 'survival'
## 
## The following object is masked from 'package:caret':
## 
##     cluster
## 
## Loading required package: Formula

## Warning: package 'Formula' was built under R version 3.1.3

## 
## Attaching package: 'Hmisc'
## 
## The following objects are masked from 'package:plyr':
## 
##     is.discrete, summarize
## 
## The following objects are masked from 'package:base':
## 
##     format.pval, round.POSIXt, trunc.POSIXt, units

#Min and max
bins <- cut(mean.prec,breaks=5)
interval_labels <- levels(cut(mean.prec,breaks=5))

bin_interval_mean <- function(interval_label){
    lower_and_upper <- unlist(strsplit(interval_label, ",", fixed = TRUE))   
    lower <- as.numeric(substr(lower_and_upper[1],start=2,stop=nchar(lower_and_upper[1])))
    upper <- as.numeric(substr(lower_and_upper[2],start=1,stop=nchar(lower_and_upper[2])-1))
    return (mean(c(lower,upper)))
}

centroids[1,1:ncol(centroids)] <- bin_interval_mean(interval_labels[1])
centroids[2,1:ncol(centroids)] <- bin_interval_mean(interval_labels[2])
centroids[3,1:ncol(centroids)] <- bin_interval_mean(interval_labels[3])
centroids[4,1:ncol(centroids)] <- bin_interval_mean(interval_labels[4])
centroids[5,1:ncol(centroids)] <- bin_interval_mean(interval_labels[5])

vector$clusterid <- bins

#Order the centroids in crescent order of intensity and reset rows
ordered.centroids <- as.numeric(names(sort(rowSums(centroids))))
centroids <- centroids[ordered.centroids,]
rownames(centroids) <- seq(1,nclusters)

#Update the centroid ids, by mapping the row ids before ordering to after ordering (element position is old ordering, value is new ordering)
vector$clusterid <- ordered.centroids[vector$clusterid]

Transitions

Define the istransition variable by observing if the previous day had a different clusterid than the current.

vector$istransition <- vector$clusterid
for (i in 2:length(vector$istransition)){
    if(vector$istransition[i-1] == vector$istransition[i])
        vector$istransition[i] <- 0 #False for C++
    else
        vector$istransition[i] <- 1 #True for C++
}
vector$istransition[1] <- -9999 # NA for C++
vector.prec <- vector

Summary

Sample 5 vectors.

kable(vector[sample(nrow(vector),5),])

	YEAR	MON	DAY	8	9	10	11	12	13	14	15	16	17	clusterid	istransition
598	2005	3	25	34.11	34.12	34.13	34.13	34.13	34.32	34.34	34.34	34.39	34.49	2	1
2592	2010	10	19	15.42	15.42	15.42	15.42	15.42	15.42	15.42	15.42	15.42	15.42	1	1
2543	2010	8	31	13.70	13.70	13.70	13.70	13.70	13.70	13.70	13.70	13.70	13.70	1	0
2595	2010	10	22	15.42	15.42	15.42	15.42	15.42	15.42	15.42	15.42	15.42	15.52	1	0
3543	2013	7	12	29.59	29.59	29.59	29.59	29.59	29.59	29.60	29.60	29.61	29.62	2	1

Centroids.

centroids_plot <- data.frame(centroids)
centroids_plot$color <-rownames(centroids)
colnames(centroids_plot) <- c(colnames(centroids),"Profile")
kable(centroids)

8	9	10	11	12	13	14	15	16	17
10.0495	10.0495	10.0495	10.0495	10.0495	10.0495	10.0495	10.0495	10.0495	10.0495
30.3000	30.3000	30.3000	30.3000	30.3000	30.3000	30.3000	30.3000	30.3000	30.3000
50.5000	50.5000	50.5000	50.5000	50.5000	50.5000	50.5000	50.5000	50.5000	50.5000
70.7000	70.7000	70.7000	70.7000	70.7000	70.7000	70.7000	70.7000	70.7000	70.7000
90.9000	90.9000	90.9000	90.9000	90.9000	90.9000	90.9000	90.9000	90.9000	90.9000

ggparcoord(
  data = centroids_plot,
  columns=c(1:(ncol(centroids)-1)),
  scale="globalminmax",
  alphaLines=0.2,
  groupColumn=ncol(centroids_plot)
) + geom_line(size=2,alpha=0.6) + science_theme + xlab("Hour of the Day") +
  ylab("Precipitation (in)") + ggtitle(paste('SCBH1',variable,'Centroids'))# + theme(axis.text.x = element_text(hjust = 1, size=20,color="black"), axis.text.y = element_text(hjust = 1, size=20,color="black"), plot.title = element_text(size=20), axis.title.y=element_text(size=20), axis.title.x=element_text(size=20))

Data Preparation Parameters

variable <- "TMPF"
nclusters <- 5

TMPF

Vectorization

Vectorize SCBH1 over the training and test partitions.

vector <- station.vectorize(SCBH1,variable)
vector <- vector[,c(1:3,12:21)]

Cleansing

If TMPF is missing any hour value, the day is discarded.

vector <- vector[complete.cases(vector),]

Discretization 5-means

Cluster with 5-means the dataset to obtain the daily profiles.

#Set seed for reproducibility
set.seed(1234)
kmeans.results <- kmeans(vector[,4:ncol(vector)],nclusters)
vector$clusterid <- kmeans.results[[1]]
centroids <- kmeans.results[[2]]

#Order the centroids in crescent order of intensity and reset rows
ordered.centroids <- as.numeric(names(sort(rowSums(centroids))))
centroids <- centroids[ordered.centroids,]
rownames(centroids) <- seq(1,nclusters)

#Update the centroid ids, by mapping the row ids before ordering to after ordering (element position is old ordering, value is new ordering)
vector$clusterid <- ordered.centroids[vector$clusterid]

Transitions

Define the istransition variable by observing if the previous day had a different clusterid than the current.

vector$istransition <- vector$clusterid
for (i in 2:length(vector$istransition)){
    if(vector$istransition[i-1] == vector$istransition[i])
        vector$istransition[i] <- 0 #False for C++
    else
        vector$istransition[i] <- 1 #True for C++
}
vector$istransition[1] <- -9999 # NA for C++
vector.tmpfc <- vector

Summary

Sample 5 vectors.

kable(vector[sample(nrow(vector),5),])

	YEAR	MON	DAY	8	9	10	11	12	13	14	15	16	17	clusterid	istransition
2676	2011	1	24	65	74	75	74	73	75	78	75	79	72	5	1
43	2003	2	12	67	69	72	74	77	77	73	72	69	69	5	1
1088	2006	8	27	79	79	82	80	80	75	81	84	84	83	2	1
2779	2011	5	7	69	67	66	70	70	72	72	71	69	67	1	1
2176	2009	8	19	80	82	81	84	79	84	82	85	83	78	2	1

Centroids.

centroids_plot <- data.frame(centroids)
centroids_plot$color <-rownames(centroids)
colnames(centroids_plot) <- c(colnames(centroids),"Profile")
kable(centroids)

8	9	10	11	12	13	14	15	16	17
67.13850	68.49307	69.43490	69.95014	70.36288	70.27978	69.75069	69.44875	68.65651	67.13573
69.35815	72.10112	73.48034	74.34129	74.50421	74.40590	74.03511	73.20365	72.16433	70.33427
72.90111	75.43667	76.67667	77.17444	77.31222	77.10889	76.58000	75.60333	74.27556	72.24000
76.27563	77.96518	79.05609	79.55416	79.69439	79.51451	79.10928	78.37041	77.02998	75.00193
78.60000	80.42192	81.70411	82.57808	82.81918	82.69452	82.26712	81.31370	79.62603	77.17808

ggparcoord(
  data = centroids_plot,
  columns=c(1:(ncol(centroids)-1)),
  scale="globalminmax",
  alphaLines=0.2,
  groupColumn=ncol(centroids_plot)
) + geom_line(size=2,alpha=0.6) + science_theme + xlab("Hour of the Day") +
  ylab("Solar Radiation Itensity (W/m2") + ggtitle(paste('SCBH1',variable,'Centroids'))# + theme(axis.text.x = element_text(hjust = 1, size=20,color="black"), axis.text.y = element_text(hjust = 1, size=20,color="black"), plot.title = element_text(size=20), axis.title.y=element_text(size=20), axis.title.x=element_text(size=20))

Data Preparation Parameters

variable <- "DWPF"
nclusters <- 5

DWPF

Vectorization

Vectorize SCBH1 over the training and test partitions.

vector <- station.vectorize(SCBH1,variable)
vector <- vector[,c(1:3,12:21)]

Cleansing

If DWPF is missing any hour value, the day is discarded.

vector <- vector[complete.cases(vector),]

Discretization 5-means

Cluster with 5-means the dataset to obtain the daily profiles.

#Set seed for reproducibility
set.seed(1234)
kmeans.results <- kmeans(vector[,4:ncol(vector)],nclusters)
vector$clusterid <- kmeans.results[[1]]
centroids <- kmeans.results[[2]]

#Order the centroids in crescent order of intensity and reset rows
ordered.centroids <- as.numeric(names(sort(rowSums(centroids))))
centroids <- centroids[ordered.centroids,]
rownames(centroids) <- seq(1,nclusters)

#Update the centroid ids, by mapping the row ids before ordering to after ordering (element position is old ordering, value is new ordering)
vector$clusterid <- ordered.centroids[vector$clusterid]

Transitions

Define the istransition variable by observing if the previous day had a different clusterid than the current.

vector$istransition <- vector$clusterid
for (i in 2:length(vector$istransition)){
    if(vector$istransition[i-1] == vector$istransition[i])
        vector$istransition[i] <- 0 #False for C++
    else
        vector$istransition[i] <- 1 #True for C++
}
vector$istransition[1] <- -9999 # NA for C++
vector.dwpfc <- vector

Summary

Sample 5 vectors.

kable(vector[sample(nrow(vector),5),])

	YEAR	MON	DAY	8	9	10	11	12	13	14	15	16	17	clusterid	istransition
2675	2011	1	23	59.0	58.0	60.5	57.7	58.2	59.6	62.0	60.8	60.6	60.0	2	1
43	2003	2	12	62.7	60.4	59.6	60.1	58.5	56.9	56.4	58.7	57.2	58.4	2	1
1086	2006	8	25	65.8	62.1	63.0	64.6	65.9	65.1	65.1	66.2	66.2	65.2	4	1
2777	2011	5	5	63.2	64.6	65.3	64.1	62.0	62.9	65.1	63.4	64.5	63.8	4	1
2174	2009	8	17	66.6	67.2	65.2	64.7	63.0	64.5	64.6	63.8	63.4	65.3	4	1

Centroids.

centroids_plot <- data.frame(centroids)
centroids_plot$color <-rownames(centroids)
colnames(centroids_plot) <- c(colnames(centroids),"Profile")
kable(centroids)

8	9	10	11	12	13	14	15	16	17
56.47021	56.11543	55.42181	55.10904	55.12606	55.02553	54.84309	54.98723	55.25426	55.26862
62.44886	61.62593	60.80012	60.42569	60.23385	60.23157	60.09952	60.20312	60.31441	60.41080
65.61374	64.79147	64.23080	63.84224	63.60427	63.56928	63.52449	63.53191	63.53191	63.71561
68.31388	67.76972	67.22666	66.99829	66.98410	66.95231	66.96891	66.89527	66.96036	66.97736
71.74212	71.39322	70.98993	70.86593	71.08590	71.11685	71.01172	70.87692	70.70989	70.48535

ggparcoord(
  data = centroids_plot,
  columns=c(1:(ncol(centroids)-1)),
  scale="globalminmax",
  alphaLines=0.2,
  groupColumn=ncol(centroids_plot)
) + geom_line(size=2,alpha=0.6) + science_theme + xlab("Hour of the Day") +
  ylab("Solar Radiation Itensity (W/m2") + ggtitle(paste('SCBH1',variable,'Centroids'))# + theme(axis.text.x = element_text(hjust = 1, size=20,color="black"), axis.text.y = element_text(hjust = 1, size=20,color="black"), plot.title = element_text(size=20), axis.title.y=element_text(size=20), axis.title.x=element_text(size=20))

Data Preparation Parameters

variable <- "RELH"
nclusters <- 5

RELH

Vectorization

Vectorize SCBH1 over the training and test partitions.

vector <- station.vectorize(SCBH1,variable)
vector <- vector[,c(1:3,12:21)]

Cleansing

If RELH is missing any hour value, the day is discarded.

vector <- vector[complete.cases(vector),]

Discretization 5-means

Cluster with 5-means the dataset to obtain the daily profiles.

#Set seed for reproducibility
set.seed(1234)
kmeans.results <- kmeans(vector[,4:ncol(vector)],nclusters)
vector$clusterid <- kmeans.results[[1]]
centroids <- kmeans.results[[2]]

#Order the centroids in crescent order of intensity and reset rows
ordered.centroids <- as.numeric(names(sort(rowSums(centroids))))
centroids <- centroids[ordered.centroids,]
rownames(centroids) <- seq(1,nclusters)

#Update the centroid ids, by mapping the row ids before ordering to after ordering (element position is old ordering, value is new ordering)
vector$clusterid <- ordered.centroids[vector$clusterid]

Transitions

Define the istransition variable by observing if the previous day had a different clusterid than the current.

vector$istransition <- vector$clusterid
for (i in 2:length(vector$istransition)){
    if(vector$istransition[i-1] == vector$istransition[i])
        vector$istransition[i] <- 0 #False for C++
    else
        vector$istransition[i] <- 1 #True for C++
}
vector$istransition[1] <- -9999 # NA for C++
vector.relh <- vector

Summary

Sample 5 vectors.

kable(vector[sample(nrow(vector),5),])

	YEAR	MON	DAY	8	9	10	11	12	13	14	15	16	17	clusterid	istransition
2674	2011	1	22	92	70	63	57	54	53	56	56	60	68	2	1
43	2003	2	12	86	74	65	62	53	50	56	63	66	69	2	1
1088	2006	8	27	75	68	61	62	63	98	73	67	63	53	2	1
2776	2011	5	4	82	70	73	68	69	65	68	67	74	83	1	1
2175	2009	8	18	79	100	91	57	53	50	59	61	69	72	1	0

Centroids.

centroids_plot <- data.frame(centroids)
centroids_plot$color <-rownames(centroids)
colnames(centroids_plot) <- c(colnames(centroids),"Profile")
kable(centroids)

8	9	10	11	12	13	14	15	16	17
65.57185	58.77502	55.11298	52.95144	52.12983	52.01685	52.52032	54.11893	57.10505	62.23092
76.48750	68.47768	63.97768	61.47321	60.60893	60.65268	61.32589	63.16429	66.18750	71.51071
88.66667	82.81530	77.44012	73.56277	71.20924	69.55988	69.36219	70.34921	73.37374	78.84993
78.82600	70.20755	66.13627	66.55975	69.77149	74.46960	78.07547	81.62474	85.66876	89.92243
94.30963	91.15138	88.83716	87.88991	87.81422	88.42202	88.97477	89.85321	90.91743	94.12156

ggparcoord(
  data = centroids_plot,
  columns=c(1:(ncol(centroids)-1)),
  scale="globalminmax",
  alphaLines=0.2,
  groupColumn=ncol(centroids_plot)
) + geom_line(size=2,alpha=0.6) + science_theme + xlab("Hour of the Day") +
  ylab("Solar Radiation Itensity (W/m2") + ggtitle(paste('SCBH1',variable,'Centroids'))# + theme(axis.text.x = element_text(hjust = 1, size=20,color="black"), axis.text.y = element_text(hjust = 1, size=20,color="black"), plot.title = element_text(size=20), axis.title.y=element_text(size=20), axis.title.x=element_text(size=20))

Data Preparation Parameters

variable <- "SKNT"
nclusters <- 5

SKNT

Vectorization

Vectorize SCBH1 over the training and test partitions.

vector <- station.vectorize(SCBH1,variable)
vector <- vector[,c(1:3,12:21)]

Cleansing

If SKNT is missing any hour value, the day is discarded.

vector <- vector[complete.cases(vector),]

Discretization 5-means

Cluster with 5-means the dataset to obtain the daily profiles.

#Set seed for reproducibility
set.seed(1234)
kmeans.results <- kmeans(vector[,4:ncol(vector)],nclusters)
vector$clusterid <- kmeans.results[[1]]
centroids <- kmeans.results[[2]]

#Order the centroids in crescent order of intensity and reset rows
ordered.centroids <- as.numeric(names(sort(rowSums(centroids))))
centroids <- centroids[ordered.centroids,]
rownames(centroids) <- seq(1,nclusters)

#Update the centroid ids, by mapping the row ids before ordering to after ordering (element position is old ordering, value is new ordering)
vector$clusterid <- ordered.centroids[vector$clusterid]

Transitions

Define the istransition variable by observing if the previous day had a different clusterid than the current.

vector$istransition <- vector$clusterid
for (i in 2:length(vector$istransition)){
    if(vector$istransition[i-1] == vector$istransition[i])
        vector$istransition[i] <- 0 #False for C++
    else
        vector$istransition[i] <- 1 #True for C++
}
vector$istransition[1] <- -9999 # NA for C++
vector.wind <- vector

Summary

Sample 5 vectors.

kable(vector[sample(nrow(vector),5),])

	YEAR	MON	DAY	8	9	10	11	12	13	14	15	16	17	clusterid	istransition
2501	2010	7	20	3	5	8	9	11	12	11	10	9	8	5	1
40	2003	2	9	1	2	6	5	6	5	6	3	4	7	1	1
1002	2006	6	2	5	2	4	5	9	7	10	8	8	7	4	1
2588	2010	10	15	3	10	6	9	8	8	8	8	7	6	4	1
2047	2009	4	12	7	7	9	7	11	11	9	8	6	7	5	1

Centroids.

centroids_plot <- data.frame(centroids)
centroids_plot$color <-rownames(centroids)
colnames(centroids_plot) <- c(colnames(centroids),"Profile")
kable(centroids)

8	9	10	11	12	13	14	15	16	17
3.229682	4.238516	4.935512	5.522968	5.802120	5.905477	5.725265	5.361307	4.752650	3.852473
4.404416	6.048580	7.101577	7.807571	8.184227	8.439748	8.328707	7.994953	7.157098	5.984227
6.879090	8.610242	9.785206	10.186344	10.492176	10.550498	10.302987	9.911807	9.012802	7.752489
48.045454	45.136364	50.772727	59.363636	66.227273	56.090909	56.090909	48.227273	47.545454	47.318182
58.461539	78.846154	87.846154	102.153846	99.230769	95.615385	88.923077	92.307692	80.307692	69.692308

ggparcoord(
  data = centroids_plot,
  columns=c(1:(ncol(centroids)-1)),
  scale="globalminmax",
  alphaLines=0.2,
  groupColumn=ncol(centroids_plot)
) + geom_line(size=2,alpha=0.6) + science_theme + xlab("Hour of the Day") +
  ylab("Solar Radiation Itensity (W/m2") + ggtitle(paste('SCBH1',variable,'Centroids'))# + theme(axis.text.x = element_text(hjust = 1, size=20,color="black"), axis.text.y = element_text(hjust = 1, size=20,color="black"), plot.title = element_text(size=20), axis.title.y=element_text(size=20), axis.title.x=element_text(size=20))

Mutual Information

Solar Cluster x Solar Cluster

Summary

Centroids

entropy <- read.table("~/Desktop/OCI/out/entropy-tsmissing.txt",header=TRUE)
ggplot(entropy, aes(x=Lag, y=Ixy)) + geom_line(colour="#94003C") + science_theme + ggtitle("Point-to-Point Mutual Information in Days") + xlab(expression(tau)) + ylab(expression(I(x[t],x[t+tau])))

++ Solar Cluster x Solar Transition

+++ Data Preparation

solar <- vector.solar[,c("YEAR","MON","DAY","istransition")]
solarc <- vector.solar[,c("YEAR","MON","DAY","clusterid")]
write.table(vector.solar[,c("clusterid","istransition")],file="~/Desktop/OCI/data/solarCid_solarTransition.csv",col.names=FALSE,row.names=FALSE,sep=",")

+++ Summary

entropy <- read.table("~/Desktop/OCI/out/entropy-solarCid_solarTransition.txt",header=TRUE)
ggplot(entropy, aes(x=Lag, y=Ixy)) + geom_line(colour="#94003C") + science_theme + ggtitle("Point-to-Point Mutual Information in Days") + xlab(expression(tau)) + ylab(expression(I(x[t],x[t+tau])))

++ Humidity Transition x Solar Transition

+++ Data Preparation

#Get the Humidity Transitions
relh <- vector.relh[,c("YEAR","MON","DAY","istransition")]

#Inner Join with the Solar Transitions
pair <- merge(relh,solar,by=c("YEAR","MON","DAY"))[,c("istransition.x","istransition.y")]
colnames(pair) <- c("relht","solart")

#Save
write.table(pair[,c("relht","solart")],file="~/Desktop/OCI/data/humidityTransition_SolarTransition.csv",col.names=FALSE,row.names=FALSE,sep=",")

+++ Summary

entropy <- read.table("~/Desktop/OCI/out/entropy-humidityTransition_SolarTransition.txt",header=TRUE)
ggplot(entropy, aes(x=Lag, y=Ixy)) + geom_line(colour="#94003C") + science_theme + ggtitle("Point-to-Point Mutual Information in Days") + xlab(expression(tau)) + ylab(expression(I(x[t],x[t+tau])))

Humidity Centroid x Solar Centroid

#Get the Humidity Transitions
relhc <- vector.relh[,c("YEAR","MON","DAY","clusterid")]

#Inner Join with the Solar and Humidity Centroids
pair <- merge(relhc,solarc,by=c("YEAR","MON","DAY"))[,c("clusterid.x","clusterid.y")]
colnames(pair) <- c("relhc","solarc")

#Save
write.table(pair[,c("relhc","solarc")],file="~/Desktop/OCI/data/humidityCentroid_SolarCentroid.csv",col.names=FALSE,row.names=FALSE,sep=",")

Summary

entropy <- read.table("~/Desktop/OCI/out/entropy-humidityCentroid_SolarCentroid.txt",header=TRUE)
ggplot(entropy, aes(x=Lag, y=Ixy)) + geom_line(colour="#94003C") + science_theme + ggtitle("Point-to-Point Mutual Information in Days in Days") + xlab(expression(tau)) + ylab(expression(I(x[t],x[t+tau])))

Temperature Centroid x Solar Centroid

#Get the Temperature Centroids
tmpfc <- vector.tmpfc[,c("YEAR","MON","DAY","clusterid")]

#Inner Join with the Solar and Temperature Centroids
pair <- merge(tmpfc,solarc,by=c("YEAR","MON","DAY"))[,c("clusterid.x","clusterid.y")]
colnames(pair) <- c("tmpfc","solarc")

#Save
write.table(pair[,c("tmpfc","solarc")],file="~/Desktop/OCI/data/temperatureCentroid_SolarCentroid.csv",col.names=FALSE,row.names=FALSE,sep=",")

Summary

entropy <- read.table("~/Desktop/OCI/out/entropy-temperatureCentroid_SolarCentroid.txt",header=TRUE)
ggplot(entropy, aes(x=Lag, y=Ixy)) + geom_line(colour="#94003C") + science_theme + ggtitle("Point-to-Point Mutual Information in Days") + xlab(expression(tau)) + ylab(expression(I(x[t],x[t+tau])))

Dew Point Centroid x Solar Centroid

#Get the Humidity Transitions
dwpfc <- vector.dwpfc[,c("YEAR","MON","DAY","clusterid")]

#Inner Join with the Solar and Humidity Centroids
pair <- merge(dwpfc,solarc,by=c("YEAR","MON","DAY"))[,c("clusterid.x","clusterid.y")]
colnames(pair) <- c("dwpfc","solarc")

#Save
write.table(pair[,c("dwpfc","solarc")],file="~/Desktop/OCI/data/dewpointCentroid_SolarCentroid.csv",col.names=FALSE,row.names=FALSE,sep=",")

Summary

entropy <- read.table("~/Desktop/OCI/out/entropy-dewpointCentroid_SolarCentroid.txt",header=TRUE)
ggplot(entropy, aes(x=Lag, y=Ixy)) + geom_line(colour="#94003C") + science_theme + ggtitle("Point-to-Point Mutual Information in Days") + xlab(expression(tau)) + ylab(expression(I(x[t],x[t+tau])))

Precipitation 5-Bins x Solar Centroid

#Get the Humidity Transitions
precc <- vector.prec[,c("YEAR","MON","DAY","clusterid")]

#Inner Join with the Solar and Humidity Centroids
pair <- merge(precc,solarc,by=c("YEAR","MON","DAY"))[,c("clusterid.x","clusterid.y")]
colnames(pair) <- c("precc","solarc")

#Save
write.table(pair[,c("precc","solarc")],file="~/Desktop/OCI/data/precipitationCentroid_SolarCentroid.csv",col.names=FALSE,row.names=FALSE,sep=",")

Summary

entropy <- read.table("~/Desktop/OCI/out/entropy-precipitationCentroid_SolarCentroid.txt",header=TRUE)
ggplot(entropy, aes(x=Lag, y=Ixy)) + geom_line(colour="#94003C") + science_theme + ggtitle("Point-to-Point Mutual Information in Days in Days") + xlab(expression(tau)) + ylab(expression(I(x[t],x[t+tau])))

++ Wind Speed Transition x Solar Transition

+++ Data Preparation

#Get the wind Transitions
wind <- vector.wind[,c("YEAR","MON","DAY","istransition")]

#Inner Join with the Solar Transitions
pair <- merge(wind,solar,by=c("YEAR","MON","DAY"))[,c("istransition.x","istransition.y")]
colnames(pair) <- c("windt","solart")

#Save
write.table(pair[,c("windt","solart")],file="~/Desktop/OCI/data/windTransition_SolarTransition.csv",col.names=FALSE,row.names=FALSE,sep=",")

+++ Summary

entropy <- read.table("~/Desktop/OCI/out/entropy-windTransition_SolarTransition.txt",header=TRUE)
ggplot(entropy, aes(x=Lag, y=Ixy)) + geom_line(colour="#94003C") + science_theme + ggtitle("Point-to-Point Mutual Information in Days") + xlab(expression(tau)) + ylab(expression(I(x[t],x[t+tau])))

Wind Centroid x Solar Centroid

Data Preparation

#Get the wind Transitions
windc <- vector.wind[,c("YEAR","MON","DAY","clusterid")]

#Inner Join with the Solar Transitions
pair <- merge(windc,solarc,by=c("YEAR","MON","DAY"))[,c("clusterid.x","clusterid.y")]
colnames(pair) <- c("windc","solarc")

#Save
write.table(pair[,c("windc","solarc")],file="~/Desktop/OCI/data/windCentroid_SolarCentroid.csv",col.names=FALSE,row.names=FALSE,sep=",")

Summary

entropy <- read.table("~/Desktop/OCI/out/entropy-windCentroid_SolarCentroid.txt",header=TRUE)
ggplot(entropy, aes(x=Lag, y=Ixy)) + geom_line(colour="#94003C") + science_theme + ggtitle("Point-to-Point Mutual Information in Days") + xlab(expression(tau)) + ylab(expression(I(x[t],x[t+tau])))

Predictive Information

Carlos V. A. Silva

June 18, 2015

Required Libraries

SOLR

Vectorization

Cleansing

Discretization 5-means

Transitions

Summary

PREC

Vectorization

Cleansing

Discretization Binning (since Centroids are constant lines)

Transitions

Summary

TMPF

Vectorization

Cleansing

Discretization 5-means

Transitions

Summary

DWPF

Vectorization

Cleansing

Discretization 5-means

Transitions

Summary

RELH

Vectorization

Cleansing

Discretization 5-means

Transitions

Summary

SKNT

Vectorization

Cleansing

Discretization 5-means

Transitions

Summary

Mutual Information

Solar Cluster x Solar Cluster

Summary

Humidity Centroid x Solar Centroid

Summary

Temperature Centroid x Solar Centroid

Summary

Dew Point Centroid x Solar Centroid

Summary

Precipitation 5-Bins x Solar Centroid

Summary

Wind Centroid x Solar Centroid

Data Preparation

Summary