I need to forecast sales on daily basis using arima model with independent variables as weekdays.
So i build up the model :
d= data,
Total = sales Monday,tuesday...Sunday are my independent Vars
i am using library(forecast)
'fit=arima(d$Total,xreg=cbind(Sunday,Monday,Tuesday,Wednesday,Thursday,Friday),order=c(1,1,1))'
Please help me to proceed further and to predict future values.
How to decide p,d,q and to plot forecasted Vs Actual Values ? please help
Related
I have a question concerning ARIMA Errors in ARIMAX models. My data and the model are coming from this site which is based on the forecast package. The model has "expenses" as the endogenous variable and "income" as the exogenous variable where the regression error term is modelled by ARIMA(1,0,2) to handle autocorrelation.
I want to know in detail how the y_t+1 value is forecasted. I know that I am using plausible future values of the exogenous variable "income" and the y_t value since we are having an ARIMA(1,0,2) model. But which values do I use for the eta_t-1 and epsilon_t, epsilon_t-1, epsilon_t-2?
I know that there are residuals listed in the model output (output of "fit$residuals" on the website) but do they belong to eta or epsilon?
I hope I was able to present my problem clearly.
Kind Regards
I need to predict the R&D expenses for a company with 5000 employees. I already created the linear regression model as follows:
lrmodel_2 <- lm(Data1_3$RD~Data1_3$employees, data = Data1_3)
summary(lrmodel_2)
I then used the predict function to predict expenses for 5000 employees. However when I try to predict I get a table with multiple values instead of 1:
(predict function result image)
Any help would be very much appreciated.
Given the following code I can obtain the "fitted" level.
fit <- ets(USAccDeaths)
fit$states[,"l"]
I would like to get the future forecasted level. The problem is the "states" object in the forecast object does not have the forecasted level.
fcast <- forecast(fit, h = 3)
fcast$model$states
How can I get the forecasted level?
The equation for the level includes the forecast error at that time. Since you don't know the forecast error until you observe an actual, you can't know the level for future times.
Forecasts from ETS models use the final value of each state in the forecast equation. So if you want a forecast of the level, use the last observed value.
I have seasonal time series data for weekly retail sales and am trying to use the tslm function of Hyndman's forecast package to fit a model with regressors in addition to trend and season.
The issue I'm running into is that when I build the tslm, before adding any regressors (only trend + season), I get a perfect fit (R^2 =1) on the training data!
A prefect fit is problematic because any additional covariate I add to the model (# of items being sold, distribution, etc.) have no impact on predictions (insignificant). Just looking at the data, I know these other regressors matter so I'm not exactly sure where I'm going wrong. Hoping somebody in the community can help me out.
Some information about the data I am using:
Dataset contains weekly data from 2014 - 2017
Training data contains 156 weekly observations (2014 - 2016)
Test data contains 48 observations in 2017
I am using weekly seasonality to build the time series:
ts.train <- ts(df.train$sales, freq=365.25/7)
m.lm <- tslm(ts.train ~ trend + season + items, data=df.train)
p.lm <- forecast(m.lm,
h=48,
newdata=data.frame(items=df.test$items))
If I leave "items" out of the formula, the predictions do not change at all.
I appreciate any input and guidance!
Items probably has too many variables (if they are dummy variables), since you get a perfect fit. See: https://www.otexts.org/fpp2/useful-predictors.html
For example, you need only 6 dummy variables to account for 7 week days.
I used to forecast sales of computers at a weekly level in SAS, based on broadly two parameters - Pricing and Marketing spends (vehicle level - hence several variables). This was easy in SAS as I could use PROC ARIMA.
Could you help me to transition to R? I have imported the dataset, performed the auto.arima and analysed p - values for some variables. However I am unaware as to how to proceed with forecasting, for the next 26 weeks. Any help would be greatly appreciated!
R has a built-in ARIMAX procedure called arima. To get the X part, use the xreg= argument. If you don't have exogenous variables and don't use xreg=, note that the the "Intercept" result may not indicate what you think it indicates.
So if you're using a ARIMAX(1, 2, 3)(1, 0, 0) model with dependent variable sales (monthly data), and an exogenous variable nasdaq (and you have a prediction for nasdaq of nasdaq.pred), you'd do:
model <- arima (sales, order=c(1, 2, 3), seasonal=list (order=c(1, 0, 0), freq=12),
xreg=nasdaq)
pred <- predict (model, newxreg=nasdaq.predict)
Suppose Your ARIMA model is Testing then ot forecast for next 26 weeks is:
Forecastedvalue<-forecast.Arima(Testing, h=26)
Hope this helps