**2.338 forecasting questions.**

Empirically, simple forecasting methods such as damped trend exponential smoothing, STL, or even random walks typically outperform more complex models such as higher order ARIMA models or ML based ...

For testing I generated a very simple time series with a clear recurring pattern. I expected that auto.arima will generate a model, that can forecast that pattern, but óbviously it doesn't. Can anyone ...

The image below is a comparison between the actual data and the predicted data for a test data set. What I am unable to achieve is the way the the actual data fluctuates over a larger span. The LSTM ...

I would like to create a linear distributed lag model in order to do some forecast and also being able to interpret the results.
Unfortunately I'm a bit confused with the process I should follow....

Does somebody happen to know how to calculate the forecast with the LS formula since it got denominator? I got confused because of that.
Here's the model I've been using for the forecast.

Full disclosure: I am not a statistician, nor do I claim to be one. I am a lowly IT administrator. Please play gentle with me. :)
I am responsible for collecting and forecasting disk storage use ...

Im new using r. Im performing a kpss test on my "y" variable and running ndiffs procedure, and in both cases get 1 for the parameter "d" , but when I run auto.arima with x regressors I get 0 for the ...

The idea here is to fit a curve to production data in agriculture.
By a production curve I mean for example the output of a mine over time, peak oil production or the yield of a farm over time within ...

I am new to the field of Business Analytics. Can anyone distinguish between forecasting model, forecasting method and forecasting function? According to the book forecasting function is is an equation ...

I would like to share my analysis in order to have guidance on how to improve the time series results.
Here you will find a table comparing real values vs forecasted ones.
Below you will find the ...

I have done a training set to fit different ARIMA models and then a test set to assess their performance (with R). From what I understood, I can use the AICc to determine the best model by choosing ...

I work at an online retailer, and we sell our products by sending out push notifications to specific places about specific products. A particular notification is called an offer. I'm trying to assess ...

I am interested in model selection in a time series setting. For concreteness, suppose I want to select an ARMA model from a pool of ARMA models with different lag orders. The ultimate intent is ...

I would say I do not have a strong foundation on stats, however, I am trying to use statistical tools for my research. I am using a hidden Markov model (HMM) to forecast day-ahead (hourly) solar ...

Would it make sense to overfit a model on purpose?
Say I have a use case where I know the data will not vary much respect to the training data.
I'm thinking here about traffic prediction, where the ...

I have run a multinomial logistic model in SAS with 5 independant variables and I need to use the results from this model to make forecasts of use of care. I have used the predicted probabilities from ...

I have a time series data with two exogenous variables. I am using auto.arima from the forecast package to determine best fit. I wanted to know if I am implementing the auto.arima function correctly ...

I have a data set of weekly sales for a range of stores (all belonging to one company). I am trying to predict weekly/monthly use of several ingredients in the individual stores. The choice for what ...

I have a non - stationary time series sequence which is based on counts. To convert the sequence into stationary I applied differencing, which converted the sequence into stationary but the sequence ...

I have balanced panel data for around 130 countries, over three years. I ran a fixed effects regression using 'country' as my panel variable, and adding dummies for 'year'.
I want to forecast the ...

I've come across some recent demand forecasting approaches that present methods where instead of generating just a point forecast, the model outputs a set of forecast quantiles, or a distribution of ...

I have daily data of new member joins from 2017/01/01 to today. I was tasked to perform forecast daily new member joins for the next month (e.g. now is Jan/31, I'm to forecast for the whole Feb).
...

I am interested in forecasting with a vector error correction model (VECM). I am facing a problem of not being able to transform a cointegrated series into a VECM model using the stationary series.
...

I'm looking at the use of bookmaker odds to predict the outcome of sporting events in which only two results are possible. A problem with using bookmaker odds to predict outcomes is that they include ...

we have a given time series includes a specific type of data for example from year 1980 to 2016. Also we know that we should achieve to a predefined goal(a fixed value) in year 2025. But we don't ...

We have the following data points in variable data pertaining to a problem that we are solving:
...

There does not seem to be a standard way to deal with missing data in the context of the exponential smoothing family of models. In particular, the R implementation called ets in the forecast package ...

To test the fbprophet library, I created a very simple synthetic series and generated a model like this:
...

I'm trying to evaluate some software for forecast accuracy. It works by summing up all the orders from a number of locations for each month, then determines the best model out of a series of models ...

I am attempting to write the mathematical model for and also simulate an MA(1) process that has drift (in R).
I have referenced ARIMA (0,1,1) or (0,1,0) - or something else?, Simulation of forecasted ...

I used boosted regression trees on a dataset I was working on to predict how much a customer will spend in a given year. Here is a sample of the output:
...

I am working on a sales forecast right now and I have created 4 models but I am unsure which one to use.
I have 17 Quarters of data(4 Full years + 1 QTR) and I am only looking to forecast 2 quarters ...

I'm trying to forecast multiple time-series with a hierarchical structure using the hts package by prof. Hyndman. However, the aggregation constraints are not sums ...

I am working on a sales forecast right now and I am not sure what type of model to use.
I have 17 Quarters of data(4 Full years + 1 QTR) and I am only looking to forecast 2 quarters into the future ...

If I use Triple Exponential Smoothing with Additive Seasonality and let a statistical program optimize alpha, beta and gamma for me, is there something I can conclude about my data based on the ...

I am working on forecasting airport delays the data looks like this
It looks like there is a structural break around 2004 where theres a huge increase and then a huge decrease around 2009.
I am ...

I am using autoregressive tree model for forecasting but m confused between regression tree and autoregressive tree model. Are these same

I am using a RandomForest to forecast the power in a wind turbine. The results are improving, but i'm getting a slight "lag" between the forecast and the value itself. Is there any way to correct this?...

I have fitted a SARIMA model for daily data with 5 regressor variables. In addition, I used Fourier terms to capture the seasonal patterns in the model according to the prof. Hyndman post on daily ...

I have quarterly data on inflation from 1990 Quartal 1 to 2016 Quartal 3.
If I want to perform the pseudo out-of-sample forecasting one quarter ahead with an autoregressive function, do I have to ...

Using the forecast function in R, I make a 1-step prediction for a log-transformed data set Y, ( Y = log(X) ). This prediction gives me a mean and a 95% prediction interval.
How valid is this ...

I am trying to do time series cross validation in R using tsCV() function from the forecast package. I have a doubt regarding the forecast horizon parameter "h" in this function. If the value of h is >...

I have time series data and I used an $ARIMA(p,d,q)+X_t$ as the model to fit the data. The $X_t$ is an indicator random variable that is either 0 (when I don’t see a rare event) or 1 (when I see the ...

I am trying to forecast the univariate time series using two different neural networks. After training and testing, I have residuals (Actual - Prediction) with me for both networks say ...

I have a data set of prices, these prices vary across time and across area. I have 18 areas with 32 time periods.
What i want to do is forecast these prices, i have found that a AR(3) process fits ...

I am trying to predict the number of guests a restaurant might serve in a meal period based on the volume of business that same day from prior years (3-5 years of data), trends for the same day of the ...

Disclaimer: I know this is a long-ish post but I don't need code solutions just high level general direction approaches that are usually used in situations like these.
So let's say I want to predict ...

I am developing a prediction model for visitors of a building. Outside temperature impacts the number of visitors - but i don't know how to include it as a regressors in the model.
This is an example ...

I am using a neural network to forecast the direction of gold prices. I have created a neural network neuralnet within R. My programme runs well and i can get a prediction accuracy of about 51%. ...

- time-series
- r
- arima
- regression
- predictive-models
- prediction
- machine-learning
- exponential-smoothing
- seasonality
- neural-networks
- prediction-interval
- accuracy
- python
- garch
- autocorrelation
- cross-validation
- econometrics
- stationarity
- var
- multiple-regression
- modeling
- autoregressive
- model-selection
- error
- self-study

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