I forgot to reply to all: This is what I sent earlier to him:

"""

I would look at the data. You don't really have any information other than
what appears to be the weekly passage. I would look to see if there is any
relationship between the current value of passage and previous values of
passage.

You can look at the ACF or PACF of the time series. If these are significant
think about creating an arma model for prediction. Ie an ar(1) model would
say that there is some linear relationship between past values of passage.
an MA(1) would say that there is a relationship between the current value
and past shocks (ie differences from the expected passage value) and the
current value. An arma model combines both of these. You can use BIC/AIC to
fine to your model.

Another thing that may exist in your dataset is seasonality ie more fish
pass during the summer months than the winter months for example.

Hope that helps,
Rob

"""

Given that you don't have a strong background in statistics you would at the
very least want to think about a few things. Do you think that the current
level of weekly passage has an impact on the next week's passage? Does this
make physical sense for fish? You can test this by regressing last week's
passage on this week's passage. This would be the same as an ar(1) model.

Seasonality also makes sense, do you find that the average value in certain
months of the year or weeks of the year are higher or lower than others?

What Ben said is basically take a look at the average value and use that to
help you predict. It looks like he is basing this relative to a seasonal
idea.

AIC/BIC is a measure of the liklihood the model fit the data taking into
account how many parameters you used to create the model. AIC uses a linear
penalize function in the number of paramters and BIC uses a more strict
penalizing function in the number of paramters. The intuition is that you
can always increase the liklihood model fits the data by adding more degrees
of freedom; however, as you add more degrees of freedom you run the risk of
overfitting. AIC/BIC basically measure the trade off in additional
explanatory power of a variable with the overfitting cost of adding another
variable to the model.

-Rob

On Tue, May 11, 2010 at 2:11 PM, Ben Bolker <bolker@ufl.edu> wrote:

> Felipe Carrillo <mazatlanmexico <at> yahoo.com> writes:
>
> ## snip
>
>  In the absence of any other information, I would say your
> best bet would just be to take the weekly average across the
> previous years.  There are lots of ways to do this (tapply,
> aggregate, etc.), but cast() works:
>
> fallavg <- cast(fallmelt,value="value",WEEK~.,fun.aggregate=mean,
>                na.rm=TRUE)
> names(fallavg)[2] <- "value"
> fallavg$variable <- "predicted"
> ggplot(fallmelt,aes(WEEK,value/1000,linetype=variable,
>                     colour=variable,fill=variable)) +
>   geom_line(size=1)+
>  theme_bw() +
>  scale_x_continuous(breaks=seq(1,52,3),
>                     labels=levels(fall$week)[seq(1,52,3)],) +
>  opts(title="Fall Cumulative") +
>   labs(y="Number of fish X 1,000",x="WEEK")+
>  geom_line(data=fallavg,size=2)
>
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