Can't predict linear model in R
I can make predictions using the ARIMA model, but when I try to make a prediction for a linear model, I don't get any real predictions - it stops at the end of the dataset (which is not useful for predictions since I already know what's in the dataset data). I have found many examples on the internet where using this same code works very well, but I have not found anyone else with the same error.
library("stats")
library("forecast")
y <- data$Mfg.Shipments.Total..USA.
model_a1 <- auto.arima(y)
forecast_a1 <- forecast.Arima(model_a1, h = 12)
The above code works fine. However, when I try to make a linear model ....
model1 <- lm(y ~ Mfg.NO.Total..USA. + Mfg.Inv.Total..USA., data = data )
f1 <- forecast.lm(model1, h = 12)
I get an error that says I MUST provide a new dataset (which seems odd to me since the documentation for the forecast package says this is an optional argument).
f1 <- forecast.lm (model1, newdata = x, h = 12)
If I do this, I can get the function to work, but the forecast only predicts values for the existing data - it does not predict the next 12 periods. I also tried using the append function to add additional lines to determine if that fixes the problem, but when trying to predict a linear model, it stops immediately at the very last point in the time series.
Here's the data I'm using:
+------------+---------------------------+--------------------+---------------------+
| | Mfg.Shipments.Total..USA. | Mfg.NO.Total..USA. | Mfg.Inv.Total..USA. |
+------------+---------------------------+--------------------+---------------------+
| 2110-01-01 | 3.59746e+11 | 3.58464e+11 | 5.01361e+11 |
| 2110-01-01 | 3.59746e+11 | 3.58464e+11 | 5.01361e+11 |
| 2110-02-01 | 3.62268e+11 | 3.63441e+11 | 5.10439e+11 |
| 2110-03-01 | 4.23748e+11 | 4.24527e+11 | 5.10792e+11 |
| 2110-04-01 | 4.08755e+11 | 4.02769e+11 | 5.16853e+11 |
| 2110-05-01 | 4.08187e+11 | 4.02869e+11 | 5.18180e+11 |
| 2110-06-01 | 4.27567e+11 | 4.21713e+11 | 5.15675e+11 |
| 2110-07-01 | 3.97590e+11 | 3.89916e+11 | 5.24785e+11 |
| 2110-08-01 | 4.24732e+11 | 4.16304e+11 | 5.27734e+11 |
| 2110-09-01 | 4.30974e+11 | 4.35043e+11 | 5.28797e+11 |
| 2110-10-01 | 4.24008e+11 | 4.17076e+11 | 5.38917e+11 |
| 2110-11-01 | 4.11930e+11 | 4.09440e+11 | 5.42618e+11 |
| 2110-12-01 | 4.25940e+11 | 4.34201e+11 | 5.35384e+11 |
| 2111-01-01 | 4.01629e+11 | 4.07748e+11 | 5.55057e+11 |
| 2111-02-01 | 4.06385e+11 | 4.06151e+11 | 5.66058e+11 |
| 2111-03-01 | 4.83827e+11 | 4.89904e+11 | 5.70990e+11 |
| 2111-04-01 | 4.54640e+11 | 4.46702e+11 | 5.84808e+11 |
| 2111-05-01 | 4.65124e+11 | 4.63155e+11 | 5.92456e+11 |
| 2111-06-01 | 4.83809e+11 | 4.75150e+11 | 5.86645e+11 |
| 2111-07-01 | 4.44437e+11 | 4.40452e+11 | 5.97201e+11 |
| 2111-08-01 | 4.83537e+11 | 4.79958e+11 | 5.99461e+11 |
| 2111-09-01 | 4.77130e+11 | 4.75580e+11 | 5.93065e+11 |
| 2111-10-01 | 4.69276e+11 | 4.59579e+11 | 6.03481e+11 |
| 2111-11-01 | 4.53706e+11 | 4.55029e+11 | 6.02577e+11 |
| 2111-12-01 | 4.57872e+11 | 4.81454e+11 | 5.86886e+11 |
| 2112-01-01 | 4.35834e+11 | 4.45037e+11 | 6.04042e+11 |
| 2112-02-01 | 4.55996e+11 | 4.70820e+11 | 6.12071e+11 |
| 2112-03-01 | 5.04869e+11 | 5.08818e+11 | 6.11717e+11 |
| 2112-04-01 | 4.76213e+11 | 4.70666e+11 | 6.16375e+11 |
| 2112-05-01 | 4.95789e+11 | 4.87730e+11 | 6.17639e+11 |
| 2112-06-01 | 4.91218e+11 | 4.87857e+11 | 6.09361e+11 |
| 2112-07-01 | 4.58087e+11 | 4.61037e+11 | 6.19166e+11 |
| 2112-08-01 | 4.97438e+11 | 4.74539e+11 | 6.22773e+11 |
| 2112-09-01 | 4.86994e+11 | 4.85560e+11 | 6.23067e+11 |
| 2112-10-01 | 4.96744e+11 | 4.92562e+11 | 6.26796e+11 |
| 2112-11-01 | 4.70810e+11 | 4.64944e+11 | 6.23999e+11 |
| 2112-12-01 | 4.66721e+11 | 4.88615e+11 | 6.08900e+11 |
| 2113-01-01 | 4.51585e+11 | 4.50763e+11 | 6.25881e+11 |
| 2113-02-01 | 4.56329e+11 | 4.69574e+11 | 6.33157e+11 |
| 2113-03-01 | 5.04023e+11 | 4.92978e+11 | 6.31055e+11 |
| 2113-04-01 | 4.84798e+11 | 4.76750e+11 | 6.35643e+11 |
| 2113-05-01 | 5.04478e+11 | 5.04488e+11 | 6.34376e+11 |
| 2113-06-01 | 4.99043e+11 | 5.13760e+11 | 6.25715e+11 |
| 2113-07-01 | 4.75700e+11 | 4.69012e+11 | 6.34892e+11 |
| 2113-08-01 | 5.05244e+11 | 4.90404e+11 | 6.37735e+11 |
| 2113-09-01 | 5.00087e+11 | 5.04849e+11 | 6.34665e+11 |
| 2113-10-01 | 5.05965e+11 | 4.99682e+11 | 6.38945e+11 |
| 2113-11-01 | 4.78876e+11 | 4.80784e+11 | 6.34442e+11 |
| 2113-12-01 | 4.80640e+11 | 4.98807e+11 | 6.19458e+11 |
| 2114-01-01 | 4.56779e+11 | 4.57684e+11 | 6.36568e+11 |
| 2114-02-01 | 4.62195e+11 | 4.70312e+11 | 6.48982e+11 |
| 2114-03-01 | 5.19472e+11 | 5.25900e+11 | 6.47038e+11 |
| 2114-04-01 | 5.04217e+11 | 5.06090e+11 | 6.52612e+11 |
| 2114-05-01 | 5.14186e+11 | 5.11149e+11 | 6.58990e+11 |
| 2114-06-01 | 5.25249e+11 | 5.33247e+11 | 6.49512e+11 |
| 2114-07-01 | 4.99198e+11 | 5.52506e+11 | 6.57645e+11 |
| 2114-08-01 | 5.17184e+11 | 5.07622e+11 | 6.59281e+11 |
| 2114-09-01 | 5.23682e+11 | 5.24051e+11 | 6.55582e+11 |
| 2114-10-01 | 5.17305e+11 | 5.09549e+11 | 6.59237e+11 |
| 2114-11-01 | 4.71921e+11 | 4.70093e+11 | 6.57044e+11 |
| 2114-12-01 | 4.84948e+11 | 4.86804e+11 | 6.34120e+11 |
+------------+---------------------------+--------------------+---------------------+
Edit - Here is the code I used to add new data for prediction.
library(xts)
library(mondate)
d <- as.mondate("2115-01-01")
d11 <- d + 11
seq(d, d11)
newdates <- seq(d, d11)
new_xts <- xts(order.by = as.Date(newdates))
new_xts$Mfg.Shipments.Total..USA. <- NA
new_xts$Mfg.NO.Total..USA. <- NA
new_xts$Mfg.Inv.Total..USA. <- NA
x <- append(data, new_xts)
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Not sure if you have ever figured this out, but just in case I thought I wanted to point out what was going wrong.
documentation for forecast.lm
says:
An optional data frame in which to look for variables with which to predict. If omitted, it is assumed that the only variables are trend and season, and h are forecasts.
so not necessary if trend
u season
are your predictors.
The ARIMA model works because it uses lagging time series values in the forecast. For a linear model, it uses predictor data ( Mfg.NO.Total..USA.
and Mfg.Inv.Total..USA.
in your case) and therefore needs their corresponding future values; without them, there are no predictive independent variables.
As amended, you added these variables to your future dataset, but they still have NA values for all future points, so the projections are also NA.
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Gabe is right. You need the future meanings of your causes.
You should consider modeling the transfer function instead of regression (i.e. designed for use with cross-sectional data ). Using prewhitening your X variables (i.e. create a model for each one), you can compute the cross-contour correlation function to see any output or lag.
It is very obvious that Inv.Total is the leading variable (b ** - 1) from the standardized plot of Y and two x. When Invto moves down, so is the shipment. In addition, there is also a seasonal AR component outside of the reasons giving the data. There are several outliers, so this is a solid solution. I am the developer of this software used here, but this can be run in any tool.
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