# Questions tagged [forecasting]

Prediction of the future events. It is a special case of [prediction], in the context of [time-series].

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3answers
22k views

### How to know that your machine learning problem is hopeless?

Imagine a standard machine-learning scenario: You are confronted with a large multivariate dataset and you have a pretty blurry understanding of it. What you need to do is to make predictions ...
2answers
11k views

### Is it unusual for the MEAN to outperform ARIMA?

I recently applied a range of forecasting methods (MEAN, RWF, ETS, ARIMA and MLPs) and found that MEAN did surprisingly well. (MEAN: where all future predictions are predicted as been equal to the ...
6answers
44k views

### Best method for short time-series

I have a question related to modeling short time-series. It is not a question if to model them, but how. What method would you recommend for modeling (very) short time-series (say of length $T \leq 20$...
3answers
42k views

### Why use a certain measure of forecast error (e.g. MAD) as opposed to another (e.g. MSE)?

MAD = Mean Absolute Deviation MSE = Mean Squared Error I've seen suggestions from various places that MSE is used despite some undesirable qualities (e.g. http://www.stat.nus.edu.sg/~staxyc/T12.pdf, ...
2answers
2k views

### Simple method of forecasting number of guests given current and historical data

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 ...
3answers
6k views

### Why does minimizing the MAE lead to forecasting the median and not the mean?

From the Forecasting: Principles and Practice textbook by Rob J Hyndman and George Athanasopoulos, specifically the section on accuracy measurement: A forecast method that minimizes the MAE will ...
3answers
7k views

### Transfer function in forecasting models - interpretation

I am occupied with ARIMA modelling augmented with exogenous variables for promotional modelling purposes and i have hard time explaining it to business users. In some cases software packages end up ...
2answers
31k views

### Interpretation of mean absolute scaled error (MASE)

Mean absolute scaled error (MASE) is a measure of forecast accuracy proposed by Koehler & Hyndman (2006). $$MASE=\frac{MAE}{MAE_{in-sample, \, naive}}$$ where $MAE$ is the mean absolute error ...
3answers
19k views

### Log or square-root transformation for ARIMA

With the below dataset, I have a series which needs transforming. Easy enough. However, how do you decide which of the SQRT or LOG transformations is better? And how do you draw that conclusion? <...
1answer
916 views

### Endogeneity in forecasting

I know that omitted variable bias isn't a major problem in forecasting, but are other endogeneity issues (such as simultaneity or measurement error) going to be a problem if I am only interested in ...
2answers
30k views

### Estimate ARMA coefficients through ACF and PACF inspection

How do you estimate the appropriate forecast model for a time series by visual inspection of the ACF and PACF plots? Which one (i.e., ACF or PACF) tells the AR or the MA (or do they both)? Which part ...
2answers
8k views

### Is it possible to automate time series forecasting?

I would like to build an algorithm that would be able to analyze any time series and "automatically" choose the best traditional/statiscal forecasting method (and its parameters) for the analyzed time ...
1answer
46k views

### Detecting Outliers in Time Series (LS/AO/TC) using tsoutliers package in R. How to represent outliers in equation format?

Comments: Firstly I would like to say a big thank you to the author of the new tsoutliers package which implements Chen and Liu's time series outlier detection which was published in the Journal of ...
10answers
64k views

### What is wrong with extrapolation?

I remember sitting in stats courses as an undergrad hearing about why extrapolation was a bad idea. Furthermore, there are a variety of sources online which comment on this. There's also a mention of ...
1answer
4k views

### Why does default auto.arima stop at (5,2,5)?

The function auto.arima in the forecast package of R is a powerful tool to identify the best ...
2answers
17k views

### stochastic vs deterministic trend/seasonality in time series forecasting

I have moderate background in time series forecasting. I have looked at several forecasting books, and I don't see the following questions addressed in any of them. I have two questions: How would I ...
2answers
4k views

### How to use Dynamic Regression models in R to forecast future sales

I want to forecast the sales having 2 independent variables, x1 and x2. I want to choose between different combinations and lags, e.g: sales ~ x1 sales ~ lag(x1,-1) sales ~ lag(x1,-1) + lag(x2,-1) ...
2answers
323 views

### Is there any standard / criteria of good forecast measured by SMAPE and MASE?

I have built a forecasting model for a company. Since it is dedicated to practical usage, I prefer to use the relative error parameter (like MAPE, SMAPE, & MASE) as a measurement for my model ...
1answer
87k views

### How to apply Neural Network to time series forecasting?

I'm new to machine learning, and I have been trying to figure out how to apply neural network to time series forecasting. I have found resource related to my query, but I seem to still be a bit lost. ...
2answers
14k views

### Forecasting hourly time series with daily, weekly & annual periodicity

Major edit: I would like to say big thanks to Dave & Nick so far for their responses. The good news is that I got the loop to work (principle borrowed from Prof. Hydnman's post on batch ...
1answer
4k views

### R Time Series Forecasting: Questions regarding my output

I'm working on a forecast for the following data: ...
2answers
791 views

### How to predict the next number in a series while having additional series of data that might affect it?

Let's say we want to predict the price of Big Mac for the year 2020. We have 2 indexes that we think might make an influence to Big Mac price determination. ...
2answers
32k views

### VAR forecasting methodology

I am building a VAR model to forecast the price of an asset and would like to know whether my method is statistically sound, whether the tests I have included are relevant and if more are needed to ...
2answers
1k views

### When is it appropriate to use an improper scoring rule?

Merkle & Steyvers (2013) write: To formally define a proper scoring rule, let $f$ be a probabilistic forecast of a Bernoulli trial $d$ with true success probability $p$. Proper scoring ...
1answer
2k views

### Can you compare AIC values as long as the models are based on the same dataset?

I am doing some forecasting in R using Rob Hyndman's forecast package. The paper belonging to the package can be found here. In the paper, after explaining the automatic forecasting algorithms, the ...
1answer
12k views

### MAPE vs R-squared in regression models

Usually regression models are evaluated using $R^2$. I understand this metric can be misleading too at times but as far as I understand the first parameter we look at is $R^2$. There is another ...
0answers
233 views

### Regression: Causation vs Prediction vs Description

In my experience it seems me that the interpretation about regression, its meaning and its scope, are debatable and great confusion exist about those things. It seems me that confusions are not go ...
1answer
219 views

### Why is removing instationarities a good thing when trying to forecast a time series?

Most introductory texts or tutorials to time-series forecasting mention that one should de-trend and de-seasonalize a time series first so that it becomes stationary. It is then easier to forecast ...
4answers
53k views

### When to log transform a time series before fitting an ARIMA model

I have previously used forecast pro to forecast univariate time series, but am switching my workflow over to R. The forecast package for R contains a lot of useful functions, but one thing it doesn't ...
2answers
11k views

### Timeseries analysis procedure and methods using R

I am working on a small project where we are trying to predict the prices of commodities (Oil, Aluminium, Tin, etc.) for the next 6 months. I have 12 such variables to predict and I have data from Apr,...
2answers
8k views

### Getting started with neural networks for forecasting

I need some resources to get started on using neural networks for time series forecasting. I am wary of implementing some paper and then finding out that they have greatly over stated the potential of ...
1answer
15k views

### Explain the croston method of R

I am using crost() function of R for analyzing and forecasting intermittent demand/slow moving items time series. I am having difficulty in understanding the output....
2answers
7k views

### ARIMA estimation by hand

I'm trying to understand how the parameters are estimated in ARIMA modeling/Box Jenkins (BJ). Unfortunately none of the books that I have encountered describes the estimation procedure such as Log-...
1answer
3k views

### Why not using the R squared to measure forecast accuracy?

Why in literature usually the common accuracy measures like MAD, MSE, RMSE, MAPE ... are used. Why not using the $R^2$ (coefficient of determination)? I was thinking about the difference: By using ...
3answers
26k views

### Seasonality not taken account of in auto.arima()

I am having basically the same issue than in this thread, except one thing: The difference, in my case, is that my data is measured weekly and not daily, so the argument of a too high seasonality (> ...
3answers
11k views

### Does ARIMA require normally distributed errors or normally distributed input data?

I have two questions related to time series forecasting with ARIMA: Does ARIMA require normally distributed errors or normally distributed input data ? Are there any assumptions on input time series ...
1answer
5k views

### Forecasting hierarchical time series R package

I have to forecast a large set of (hierarchical) time series and since the R package hts allows for confidence intervals for their ensemble, I'd like to use it. I haven't found an example of how to ...
4answers
10k views

### forecast using arima models [closed]

I am trying to predict values using arima(0,1,1). After doing predict(mod,n.ahead=5) (in R) am getting the same value for all ...
1answer
3k views

### stl() gives seasonal component, but ets() and auto.arima() choose nonseasonal models

I'm completely new to forecasting so please correct me if I'm wrong. I'm trying to forecast sales data using R. My main concern is that when I decompose the data using ...
1answer
10k views

### Time Series Forecasting with Daily Data: ARIMA with regressor

I'm using a daily time series of sales data that contains about 2 years of daily data points. Based on some of the online-tutorials / examples I tried to identify the seasonality in the data. It seems ...
4answers
6k views

### Forecasting binary time series

I have a binary time series with 1 when the car is not moving, and 0 when the car is moving. I want to make a forecast for a time horizon up to 36 hours ahead and for each hour. My first approach ...
2answers
3k views

### Prediction with GLS

Let's say I build a Generalized Least Squares model. I follow the standard procedure and first estimate a LM model. Then I create an error-response covariance matrix based on the residuals of this ...
2answers
3k views

### Assigning Weights to An Averaged Forecast

So I've been learning how to forecast over this summer and I've been using Rob Hyndman's book Forecasting: principles and practice. I've been using R, but my questions aren't about code. For the ...
2answers
304 views

### What is the best point forecast for gamma distributed data?

I believe that the values I am forecasting are gamma distributed with shape $k>0$ and scale $\theta>0$. I need a point forecast (i.e., a one-number summary) that minimizes the expected error. ...
2answers
351 views

### How is Hyndman's explanation of proper Time Series Cross Validation different from Leave-One-Out?

Hyndman's great explanation of proper time series CV is at the bottom of the page in the following link: http://robjhyndman.com/hyndsight/crossvalidation/ Leave-One-Out illustration in the following ...
4answers
89k views

### Difference between forecast and prediction?

I was wondering what difference and relation are between forecast and prediction? Especially in time series and regression? For example, am I correct that: In time series, forecasting seems to mean ...
3answers
4k views

### AIC,BIC,CIC,DIC,EIC,FIC,GIC,HIC,IIC — Can I use them interchangeably?

On p. 34 of his PRNN Brian Ripley comments that "The AIC was named by Akaike (1974) as 'An Information Criterion' although it seems commonly believed that the A stands for Akaike". Indeed, when ...
4answers
4k views

### Assessing forecastability of time series

Suppose i have a little over 20.000 monthly time series spanning from Jan'05 to Dec'11. Each of these representing global sales data for a different product. What if, instead of computing forecasts ...
3answers
23k views

### How do I handle nonexistent or missing data?

I tried a forecasting method and want to check if my method is correct or not. My study is comparing different kinds of mutual funds. I want to use the GCC index as a benchmark for one of them but ...
1answer
12k views

### Forecasting a seasonal time series in R

Forecasting airline passengers seasonal time series using auto.arima() I am trying to model some airline data in an attempt to provide an accurate monthly forecast ...