diff --git a/M4 best of 7 forecast files/Forecast_best of 7.R b/M4 best of 7 forecast files/Forecast_best of 7.R
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+require(dplyr)
+require(forecast)
+require(tidyr)
+require(svMisc)
+# Run this code to load the dataset. A prompt will appear. 
+#Select the data file to load in R environment.
+#Dataset of only one type of frequency can be used at a given time.
+Data_set_m4 <- read.csv(file.choose(),stringsAsFactors = F) Data_set_m4 <- remove_empty(Data_set_m4, "cols")
+
+#Converting row wise time series to column wise in order to make it a tidy data set
+Data_set_m4.long <- as.data.frame(t(Data_set_m4[,-1]))     
+remove(Data_set_m4)
+
+#SOME NOTES BEFORE BEGGINING THE FORECASTING CODE
+# Use the code from line no 26 to 97 for yearly, quarterly, hourly and monthly frequency.
+# Use the code from line no 100 to 154 for daily and weekly frequency data (upto series no 295 for weekly)
+# Use the code after that for weekly data from series no 296 onwards
+# The loop breaks down sometimes as some methods fail for some time series
+#(specifically for quarterly variables no 9454 and 21168.)
+#Note the value of i where the loop breaks down.
+#Just run the code manually and then replace the intial value of i in the loop with next value (e.g. 9455) and rerun the loop
+
+
+#Some user Inputs required for later use.
+
+#Frequency of the data set
+data_frequency <- 5 # Use 4 for quarterly | 1 for yearly | 12 for monthly| 24 for hourly | 365.25/7 for weekly | 5 for daily financial and 7 for others
+
+#Fraction of dataset to split into training and test sets
+frac_data_train <- .9 # .8 for quarterly, yearly & monthly | .9 for hourly, weekly and daily
+#No. of final required forecasts 
+no_req_forecasts <- 14 # As per M4 rules according to data frequency
+# A parameter for seasonal frequency of dataset
+seasonal_freq <- c(5) # 4 for quarterly | 1 for yearly | 24 & 168 (c(24,168)) for hourly |365.25/7 for weekly | 5 for daily financial and 7 for others
+
+# Empty list to store combined forecasts
+combined_forecasts <- list()
+#Loop to run the entire forecasting procedure across all columns on column at a time
+for(i in 1:(ncol(Data_set_m4.long)))
+{
+
+progress(i,max.value = ncol(Data_set_m4.long)) # Progress Bar for loop
+    
+#Convert column i to a seasonal time series object
+V1ts <- msts(Data_set_m4.long[,i][!is.na(Data_set_m4.long[,i])], seasonal.periods = seasonal_freq,ts.frequency = data_frequency)
+
+#Split the time series in column i into training and test columns
+trainingdim <- 1:(floor((frac_data_train*length(V1ts))))
+V1train <- ts(V1ts[trainingdim],frequency = data_frequency)
+V1test <- V1ts[-trainingdim]
+no_forecasts <- length(V1test)
+
+# The list named accuracy_stats stores the out-of-sample accuracy statistics for forecasts generated 
+# using training set and compared with the test set
+accuracy_stats <- list()
+accuracy_stats$ets <- accuracy(forecast(stlm(V1train,s.window = "periodic",method = "ets"),no_forecasts),V1test)
+accuracy_stats$arima <- accuracy(forecast(stlm(V1train,s.window = "periodic",method = "arima"),no_forecasts),V1test)
+accuracy_stats$neural <- accuracy(forecast(nnetar(V1train),no_forecasts),V1test)
+accuracy_stats$seasnaive <- accuracy(snaive(V1train,no_forecasts),V1test)
+accuracy_stats$theta <- accuracy(thetaf(V1train,no_forecasts),V1test)
+accuracy_stats$naive <- accuracy(naive(V1train,no_forecasts),V1test)
+accuracy_stats$randomwalk <- accuracy(rwf(V1train,drift = T,no_forecasts),V1test)
+
+# Retrieve MAPE of all seven methods used to forecast
+mape_all_methods <- sapply(accuracy_stats, function(x){x[10]})
+
+# Select top performing forecasting methods according to MAPE (> median)
+# This leads to a minimum of one and a maximum of 3 selected methods
+mape_selected <- if(length(names(which(mape_all_methods < median(mape_all_methods)))) != 0)
+    { names(which(mape_all_methods < median(mape_all_methods)))} else {names(mape_all_methods) }
+
+# Assign weights to each method according to their MAPE in the previous step.
+# The higher the MAPE, the lower the weight. See document for the formula used to calculate weights.
+acc_stats_select <- sapply(accuracy_stats[mape_selected], function(x){x[10]}) # extract MAPE of selected methods
+
+acc_stats_weight <- prop.table(sapply(acc_stats_select,function(x){sum(acc_stats_select)-x})^3) # Calculate weights
+acc_stats_weight <- ifelse(acc_stats_weight %in% NaN,1,acc_stats_weight) # If only one method is selected, the previous code returns NaN. This code converts the NaN to 1.
+
+# Retrain the models using full data set.
+final_forecast <- list()
+final_forecast$ets <- forecast(stlm(V1ts,s.window = "periodic",method = "ets"),no_req_forecasts)$mean
+final_forecast$arima <- forecast(stlm(V1ts,s.window = "periodic",method = "arima"),no_req_forecasts)$mean
+final_forecast$neural <- forecast(nnetar(V1ts),no_req_forecasts)$mean
+final_forecast$seasnaive <- snaive(V1ts,no_req_forecasts)$mean
+final_forecast$theta <- thetaf(V1ts,no_req_forecasts)$mean
+final_forecast$naive <- naive(V1ts,no_req_forecasts)$mean
+final_forecast$randomwalk <- rwf(V1ts,drift = T,no_req_forecasts)$mean
+
+# Extract the forecasts of selected methods
+forecasts_selected <- sapply(final_forecast[mape_selected],function(x){x[1:no_req_forecasts]})
+# Calculate a weighted average of forecasts from the selected methods. This is the step
+# where we obtain the final forecasts. The loop Repeats this procedure for all i columns.
+combined_forecasts[[i]] <- rowSums(sweep(forecasts_selected,2,acc_stats_weight,FUN = "*"))
+}
+#Combine all forecasts row wise (M4 format) and export them to csv. Run the code again for different frequencies.
+names(combined_forecasts) <- lapply(1:length(combined_forecasts),function(x){ paste("W",x,sep="")})
+write.csv(do.call(rbind,combined_forecasts),file="weekly.csv")
+
+
+# CODE FOR DAILY AND WEEKLY DATA
+
+#Frequency of the data set
+data_frequency <- 5 # Use 4 for quarterly | 1 for yearly | 12 for monthly| 24 for hourly | 365.25/7 for weekly | 5 for daily financial and 7 for others
+
+#Fraction of dataset to split into training and test sets
+frac_data_train <- .9 # .8 for quarterly, yearly & monthly | .9 for hourly, weekly and daily
+#No. of final required forecasts 
+no_req_forecasts <- 14 # As per M4 rules according to data frequency
+# A parameter for seasonal frequency of dataset
+seasonal_freq <- c(5) # 4 for quarterly | 1 for yearly | 24 & 168 (c(24,168)) for hourly |365.25/7 for weekly | 5 for daily financial and 7 for others
+
+# Empty list to store combined forecasts
+combined_forecasts <- list()
+#Loop to run the entire forecasting procedure across all columns on column at a time
+for(i in 1:(ncol(Data_set_m4.long)))
+{
+    progress(i,max.value = ncol(Data_set_m4.long))
+    V1ts <- msts(Data_set_m4.long[,i][!is.na(Data_set_m4.long[,i])], seasonal.periods = seasonal_freq,ts.frequency = data_frequency)
+    
+    trainingdim <- 1:(floor((frac_data_train*length(V1ts))))
+    V1train <- ts(V1ts[trainingdim],frequency = data_frequency)
+    V1test <- V1ts[-trainingdim]
+    no_forecasts <- length(V1test)
+    accuracy_stats <- list()
+    accuracy_stats$ets <- accuracy(stlf(V1train,h = no_forecasts,s.window = data_frequency,method = "ets"),V1test)
+    accuracy_stats$arima <- accuracy(stlf(V1train,h = no_forecasts,s.window = data_frequency,method = "arima"),V1test)
+    accuracy_stats$neural <- accuracy(forecast(nnetar(V1train),no_forecasts),V1test)
+    accuracy_stats$seasnaive <- accuracy(snaive(V1train,no_forecasts),V1test)
+    accuracy_stats$naive <- accuracy(naive(V1train,no_forecasts),V1test)
+    accuracy_stats$randomwalk <- accuracy(rwf(V1train,drift = T,no_forecasts),V1test)
+    
+    mape_all_methods <- sapply(accuracy_stats, function(x){x[10]})
+    
+    mape_selected <- if(length(names(which(mape_all_methods < median(mape_all_methods)))) != 0)
+    { names(which(mape_all_methods < median(mape_all_methods)))} else {names(mape_all_methods) }
+    
+    acc_stats_select <- sapply(accuracy_stats[mape_selected], function(x){x[10]})
+    acc_stats_weight <- prop.table(sapply(acc_stats_select,function(x){sum(acc_stats_select)-x})^3)
+    acc_stats_weight <- ifelse(acc_stats_weight %in% NaN,1,acc_stats_weight)
+    final_forecast <- list()
+    final_forecast$ets <- stlf(V1ts,h = no_req_forecasts,s.window = data_frequency,method = "ets")$mean
+    final_forecast$arima <- stlf(V1ts,h = no_req_forecasts,s.window = data_frequency,method = "arima")$mean
+    final_forecast$neural <- forecast(nnetar(V1ts),no_req_forecasts)$mean
+    final_forecast$seasnaive <- snaive(V1ts,no_req_forecasts)$mean
+    final_forecast$naive <- naive(V1ts,no_req_forecasts)$mean
+    final_forecast$randomwalk <- rwf(V1ts,drift = T,no_req_forecasts)$mean
+    
+    forecasts_selected <- sapply(final_forecast[mape_selected],function(x){x[1:no_req_forecasts]})
+    
+    combined_forecasts[[i]] <- rowSums(sweep(forecasts_selected,2,acc_stats_weight,FUN = "*"))
+}
+#Combine all forecasts row wise (M4 format) and export them to csv. Run the code again for different frequencies.
+names(combined_forecasts) <- lapply(1:length(combined_forecasts),function(x){ paste("W",x,sep="")})
+write.csv(do.call(rbind,combined_forecasts),file="weekly.csv")
+
+
+## CODE FOR WEEKLY DATA Variable no 296 onwards because the previous code only works till variable 295
+
+#Frequency of the data set
+data_frequency <- 5 # Use 4 for quarterly | 1 for yearly | 12 for monthly| 24 for hourly | 365.25/7 for weekly | 5 for daily financial and 7 for others
+
+#Fraction of dataset to split into training and test sets
+frac_data_train <- .9 # .8 for quarterly, yearly & monthly | .9 for hourly, weekly and daily
+#No. of final required forecasts 
+no_req_forecasts <- 14 # As per M4 rules according to data frequency
+# A parameter for seasonal frequency of dataset
+seasonal_freq <- c(5) # 4 for quarterly | 1 for yearly | 24 & 168 (c(24,168)) for hourly |365.25/7 for weekly | 5 for daily financial and 7 for others
+
+# Empty list to store combined forecasts
+combined_forecasts <- list()
+#Loop to run the entire forecasting procedure across all columns on column at a time
+for(i in 1:(ncol(Data_set_m4.long)))
+{
+    progress(i,max.value = ncol(Data_set_m4.long))
+    V1ts <- msts(Data_set_m4.long[,i][!is.na(Data_set_m4.long[,i])], seasonal.periods = seasonal_freq,ts.frequency = data_frequency)
+    
+    trainingdim <- 1:(floor((frac_data_train*length(V1ts))))
+    V1train <- ts(V1ts[trainingdim],frequency = data_frequency)
+    V1test <- V1ts[-trainingdim]
+    no_forecasts <- length(V1test)
+    accuracy_stats <- list()
+    accuracy_stats$neural <- accuracy(forecast(nnetar(V1train),no_forecasts),V1test)
+    accuracy_stats$seasnaive <- accuracy(snaive(V1train,no_forecasts),V1test)
+    accuracy_stats$naive <- accuracy(naive(V1train,no_forecasts),V1test)
+    accuracy_stats$randomwalk <- accuracy(rwf(V1train,drift = T,no_forecasts),V1test)
+    
+    mape_all_methods <- sapply(accuracy_stats, function(x){x[10]})
+    
+    mape_selected <- if(length(names(which(mape_all_methods < median(mape_all_methods)))) != 0)
+    { names(which(mape_all_methods < median(mape_all_methods)))} else {names(mape_all_methods) }
+    
+    acc_stats_select <- sapply(accuracy_stats[mape_selected], function(x){x[10]})
+    acc_stats_weight <- prop.table(sapply(acc_stats_select,function(x){sum(acc_stats_select)-x})^3)
+    acc_stats_weight <- ifelse(acc_stats_weight %in% NaN,1,acc_stats_weight)
+    final_forecast <- list()
+    final_forecast$neural <- forecast(nnetar(V1ts),no_req_forecasts)$mean
+    final_forecast$seasnaive <- snaive(V1ts,no_req_forecasts)$mean
+    final_forecast$naive <- naive(V1ts,no_req_forecasts)$mean
+    final_forecast$randomwalk <- rwf(V1ts,drift = T,no_req_forecasts)$mean
+    
+    forecasts_selected <- sapply(final_forecast[mape_selected],function(x){x[1:no_req_forecasts]})
+    
+    combined_forecasts[[i]] <- rowSums(sweep(forecasts_selected,2,acc_stats_weight,FUN = "*"))
+}
+#Combine all forecasts row wise (M4 format) and export them to csv. Run the code again for different frequencies.
+names(combined_forecasts) <- lapply(1:length(combined_forecasts),function(x){ paste("W",x,sep="")})
+write.csv(do.call(rbind,combined_forecasts),file="weekly.csv")
diff --git a/M4 best of 7 forecast files/M4-Method-Description.pdf b/M4 best of 7 forecast files/M4-Method-Description.pdf
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