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Strange increasing in $R^2$ when MAE and RMSE worsened for OLS

I am currently working on my thesis, which involves using machine learning to predict non-stationary and seasonal time series. I am encountering some results that I cannot explain. While I cannot go ...
M. Hansen's user avatar
0 votes
0 answers
31 views

Autoregession meets multiple regression? - Help with verbiage and approach

Needing some help with verbiage and opinions on how I am approaching this model. I have counts of people over the past 24 months. month count 1 100 2 105 ... ... 24 200 First, I reverse the ...
Tyler Brown's user avatar
1 vote
1 answer
96 views

Smoothing out target variable for spiky demand forecasting

I am trying to predict ambulance demand for the next hour, for a city area in the USA, based on previous demand, weather, large people gatherings, and similar spatio-temporal factors - using Machine ...
Nadir Bašić's user avatar
3 votes
1 answer
151 views

In sliding window regression, what is the best way to select my training window and test set size?

I am trying to forecast an index option's implied volatility using a sliding window regression and I'm a little confused on how I can go about cross validating with respect to the training and test ...
sixth-sense-81's user avatar
0 votes
1 answer
157 views

Implementing Random Forest rolling window forecast in R [closed]

I want to forecast a dependent variable and I have some independent variables. First I shifted the dependent variable up, such that I have a supervised problem. For instance in January 2000 the ...
walk_forward_window's user avatar
3 votes
0 answers
159 views

Fixed vs rolling forecasts: Empirical evidence

Many sources online recommend that we use a rolling window to make forecasts. As I am in a choice between using a fixed window and a rolling window for my data I am trying to find any empirical ...
Johanna W's user avatar
0 votes
2 answers
810 views

Exact steps for rolling window CV evaluation or sliding window CV evaluation for SARIMA

So far I have using this process: 1)split data into training and test 2)do model selection(p,d,q, P,D,Q,etc) using training data(in this case, I used autoarima) ...
a12345's user avatar
  • 95
1 vote
2 answers
944 views

Cross Validation for Time Series Classification (Not Forecasting!)

Is it possible to use regular k-fold cross validation where the folds contain entire time series in time series classification? I'm asking because most sources discussing cross validation with time ...
perceptronEnthusiast420's user avatar
2 votes
1 answer
435 views

Lag selection and model instability for ARIMA-GARCH in rolling windows for forecasting

I'm to produce rolling forecasts with an ARIMA-GARCH model using a moving window size of 1000. Given that structural changes in the series might take place at some point in the forecast horizon, is ...
Crib's user avatar
  • 33
1 vote
1 answer
554 views

ARIMA accuracy measures, rolling forecast

Regarding ARIMA model selection and especially accuracy measures several questions came into my mind. To shortly summarize, in my understanding, after necessary transformations/differencing, p and q ...
EEEE77's user avatar
  • 69
1 vote
1 answer
236 views

Modifying tsCV function

I tried to modify the tsCV function to seperate between xreg_subset and xreg_future as Im going to use forecasted data for validate and test samples: ...
Shwan's user avatar
  • 25
3 votes
2 answers
3k views

Root-mean-square error when having multiple prediction horizons

I have a basic question about the root-mean-square error (RMSE). I have a prediction using an ARIMA model. I predicted a time series and use a rolling-horizon approach with overlapping or non-...
PeterBe's user avatar
  • 392
0 votes
1 answer
150 views

Does the method of reruning GARCH models every day (to update parameter values and improve out-of-sample forecasting performance) have a name?

It is my understanding that normally GARCH models make forecasts for say T-K days ahead. Instead of doing that I would like to use the data for days 1, 2, ...,k in my dataset to fit a GARCH model to ...
BillB's user avatar
  • 89
1 vote
2 answers
972 views

How do we forecast using 3 point moving average?

X<- c(3,6,8,10,6,5) If I want to forecast using 3point moving average I use ma(X,3) from forecast package So this is going to give a series of smoothed average. If I want to forecast further 2 ...
Manisha's user avatar
  • 80
1 vote
1 answer
703 views

Out-of-sample Rolling window forecast with ARIMA(0,0,0) with non-zero mean

I am doing a rolling window out-of-sample forecast and have fitted an ARIMA(0,0,1) model to a first difference time series. People argue that sometimes simpler models are better than more complicated ...
endorphinus's user avatar
2 votes
1 answer
6k views

ARIMA + Rolling Window

I'm currently working on building an ARIMA+GARCH model using R. My dataset consists of the logarithmic returns of the Dow Jones index for a period of 11 years 2005-2016, however, it's worth noting ...
Lundu Chibwa's user avatar
1 vote
1 answer
428 views

How does the sliding window work?

I am not sure how the "Sliding window" method work. Let's assume I have a dataset of number of logins by hour. a) A window of 24hours to predict the next 24h? b) A window of 24h to predict the next ...
Aizzaac's user avatar
  • 1,179
2 votes
2 answers
1k views

Rolling Window Forecasting with ARIMAX while supplying actual values

I am comparing different exogenous variables in how good they support the forecast of the monthly seasonal adjusted unemployment rate. All my data is monthly (2006-01-01 until 2018-09-01) and ...
Nora's user avatar
  • 21
2 votes
1 answer
282 views

Markov Switching GARCH - Expanding or Rolling window forecasting?

When modelling volatility do people tend to use expanding or sliding windows to predict the performance of MS GARCH models?
Anna's user avatar
  • 265
0 votes
0 answers
433 views

Rolling window AR(2)-GARCH(1,1) VaR intuition query

I have an AR(2)-GARCH(1,1) model and I need to use a 1-day rolling window 1 -step ahead forecasts to calculate the 5% conditional VaR. I know how to calculate the VaR for 1-step ahead only - I ...
user avatar
4 votes
2 answers
7k views

Selecting ARIMA Order using Rolling Forecast

I'm wondering if a rolling forecast technique like the ones mentioned in Rob Hyndman's blogs, and the example below, could be used to select the order for an ARIMA model? In the examples I've looked ...
ndderwerdo's user avatar
1 vote
1 answer
4k views

Rolling forecasts: training versus forecast accuracy evaluation

Questions: Are rolling forecast examples (like the ones below) only useful for evaluating a model's accuracy, or can a rolling forecast be used to train a model? Are models trained using a rolling ...
ndderwerdo's user avatar
0 votes
1 answer
876 views

Question about rolling forecast horizon

I'm trying to understand how the rolling forecast example below from Rob Hyndman's blog works. In the final line of the for loop, is ...
ndderwerdo's user avatar
4 votes
1 answer
2k views

Are rolling forecasts more accurate that full-sample forecasts?

I compared the auto.arima forecast checkts below to the rolling forecast fc and noticed ...
modLmakur's user avatar
  • 249
0 votes
1 answer
946 views

Forecast evaluation for rolling forecast [closed]

I have rolling forecast for each month. I would like to do some forecast evaluation. How do I do this?
user43790's user avatar
  • 175
6 votes
1 answer
1k views

Benchmarking time series forecasting model

Problem: I'm building a time series forecasting model for daily data wherein, the aim is to forecast for the next one week. So, to validate the model, I'm using a moving window based validation ...
psteelk's user avatar
  • 253
1 vote
1 answer
5k views

Want to make a function which allows for recursive window forecasting

I have been looking for a function that can make recursive window out-of-sample forecasts, but seems there is none. So I'm thinking about about making a function that can be used for recursive window ...
michael's user avatar
  • 13