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Questions tagged [forecasting]

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

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18
votes
1answer
1k views

$ARIMA(p,d,q)+X_t$, Simulation over Forecasting period

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 ...
10
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1answer
423 views

Adjustments to (Linear Regression) 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 ...
8
votes
1answer
2k views

How to compare forecasting methods?

I have several intermittent data. Based on those data, I would like to compare several forecasting methods (Exponential Smoothing, Moving Average, Croston, and Syntetos-Boylan), and decide whether ...
7
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0answers
895 views

Forecasting time-series ahead by multiple time horizons

Suppose that I have daily data on the population of a small village, given by $Y(t)$, as well as daily data on various factors that are relevant to the size of the population in the future, given by ...
6
votes
1answer
810 views

forecast::auto.arima() is not returning a model with a differencing parameter when it should

I'm experiencing an issue in which it seems forecast::auto.arima() isn't returning a model with a differencing parameter when it should. Read through my reproducible example to arrive at the question. ...
6
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4answers
1k views

Choice of time-series model for store sales prediction

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 ...
6
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0answers
1k views

Empirical Prediction interval for time series forecast based on quantile regression

As Gardner notes "almost all point forecasts are wrong", so prediction intervals (PI) are necessary to quantify uncertainty and help us make informed decisions. There exists theoretical PI, and in ...
6
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1answer
486 views

Forecasting daily visits using ARIMA with external regressors

I have daily visitors data for the last 10 years. I want to do some basic tests like which is the busiest day, which is the busiest month, busiest week etc. I used ...
6
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0answers
1k views

Reorder point with stochastic lead time and demand

I'm trying to determine the optimal reorder point for some products. The reorder point must be greater than the demand during lead time a $\%$ of the times that I should determine, let's say $95\%$. ...
6
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0answers
5k views

Accuracy of aggregate vs. disaggregate forecasting

I've found a few interesting articles online on this topic, but none which appear to be too cut and dry. My question is coming up with an accurate predictive forecast based on forecasting individual ...
6
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0answers
269 views

Forecasting a complex time series by splitting into subseries

I have finance data that I need to forecast out for 7 years. My data is generally debits and credits, and those are split into a number of sub-series which share common traits (e.g. similar ...
5
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0answers
135 views

Is there a theoretical reason why simple models perform better than complex models on time series forecasting tasks?

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 ...
5
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0answers
625 views

Time Series forecasting with Gaussian Processes

I am trying to forecast various time-series with Gaussian Processes, using the functional approach like in the Mauna Loa example in section 5.4.3 of "Gaussian Processes for Machine Learning". (X = ...
5
votes
0answers
1k views

Hierarchical time-series forecasting with complex aggregation constraints

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 ...
5
votes
1answer
465 views

What is the difference between ARMA+Fourier and TBATS model?

I am just wondering that, in terms of the multi-seasonal time series forecast, what is the difference between using auto.arima find the ARMA order, then fit ...
5
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0answers
2k views

Is it possible to do a time series analysis with more than one explanatory variable?

I am working on a project, and I am absolutely new to forecasting and not so strong in statistics. I have an employee data for the last 7 years, along with the other variables like economic growth, ...
5
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0answers
706 views

Seasonally adjusted data used in time series forecasting

I am looking at two time series, from 01/01/2000 to the present: The ISM Manufacturing: New Orders Index, only available seasonally adjusted The manufacturing industry unemployment rate, only ...
5
votes
1answer
732 views

How to specify when a level shift begins and ends or in the case of data series with multiple level shifts how to id when one level shift beings/ends?

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 ...
5
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0answers
162 views

Time series for network matrices?

Let's say I have two people. Each of whom I create a network matrix for daily for some of their daily habits $(i.e. A, B, C, D)$. I do this for, let's say $60$ days. Two People and their daily ...
5
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0answers
1k views

Best practices for dealing with shifting, inconsistent seasonality

This question is related to a previous post I've looked at (Calculation of seasonality indexes for complex seasonality), but deals with more granular data (daily instead of weekly), and transforming ...
4
votes
1answer
139 views

Some questions about quarterly and monthly timeseries

I need some help in my forecasting analysis. In my company, for the most part, if we take a look at monthly sales time series we will find a lot of noise, a large standard deviation and variance, ...
4
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0answers
54 views

Method to forecast correlate univariate time-series (with trend, seasonality) via regression

I have two univariate time-series with seasonality and trend--dt1 and dt2. I believe that dt1 and dt2 are strongly correlated, both through a few statistical test (see below) and that in my field dt2 ...
4
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0answers
41 views

Looking for advice: Short-term forecasting using actual forecasts and real time data

First of all apologies, I have very little experience in statistics and my biggest problem is using the correct terminology. I'm here mainly looking for guidance and direction. Background: I have a ...
4
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0answers
1k views

Rolling window time series training and validation in Keras

I have a conceptual question regarding the use of the rolling window approach for training and validating a recurrent neural network (LSTM or GRU) on time series data. I have daily time series data ...
4
votes
1answer
364 views

How to judge whether to model a time series additively or multiplicatively?

I don't know how to to identify whether my time series is additive or multiplicative using decompose() command in R. It is a monthly time series.
4
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0answers
102 views

Forecasting with annual data that has a rolling quarter

I want to fit an ARIMA model for forecasting on a quarterly basis, but my data is a rolling year, updated quarterly, how can I use this most effectively? I'm really interested in the best estimate of ...
4
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0answers
235 views

Custom metric for model selection in auto.arima

I'm using the auto.arima function of theforecast package. I would like to perform the model selection using a custom metric ...
4
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0answers
123 views

How to estimate a continuous analog of the (discrete) vector autoregression (VAR) model

I have some ten to 100 thousand observations on each of around 500 entities. I have good reason to believe that these observations all mutually influence one another, in possibly complicated ways, or ...
4
votes
1answer
472 views

Model selection and estimation for pseudo out-of-sample forecasting

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 ...
4
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0answers
905 views

Gaussian process regression and optimizing an RBF kernel for forecasting?

I'm using gaussian process regression with an RBF kernel to forecast a time series. I'm using GaussianProcessRegression in ...
4
votes
2answers
162 views

How to do regression on a time series by learning from historical time series?

I have a data set of customer purchases from the day of their registration to 120 days. There is a time series for each customer. However, some new customers do not have a history of 120 days yet. I ...
4
votes
1answer
1k views

Identifying lagged effects / Distributed Lag Model

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....
4
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0answers
378 views

Changing sensitivity (cval) in tsoutliers resulting in unexpected results

I am using the excellent tsoutliers R package to detect outliers (additive outliers, temporary changes etc.), but the cval ...
4
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0answers
635 views

Irregular Seasonality in time series

I understand seasonality of a time series normally means a cyclic component with constant frequency. For example, the frequency is 24 for daily cyclic trend of hourly data. One of the basic models ...
4
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0answers
207 views

Comparison of estimation techniques for ARIMA model

I'm a math graduate student with not much knowledge in statistics. I could note that we have different techniques to estimate ARIMA parameters for a time series: using Bayes's Theorem, maximizing the ...
4
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0answers
91 views

Is there a name for a fallacy, when a word is understood colloquially instead of technically?

I sometimes encounter a view that only perfect forecasting is really forecasting. For example, if I claim that I have a model which forecasts election results, people will think I'm making the ...
4
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0answers
1k views

Attrition Forecasting

I am currently trying to develop a forecast for monthly subscriber attrition that allows me to predict for a future point in time, how many subscribers I have. I have a couple of years worth of ...
4
votes
0answers
900 views

Analysis of Multiple Time Series Data with Exogenous Shocks

Real Life ProblemThis one is a tough one and some crowd sourcing seems like a good way to get some feedback. I am trying to determine the effect of Non-Farm Payroll surprises on a subsector of the ...
4
votes
0answers
3k views

Forecasting and decomposition of hourly time series with 2 seasonal periods

I have hourly temperature data over a 5 year period with a lot of missing values. They have 2 seasonal periods: daily (24) and annual (365*24). I am very interested in the diurnal cycles of the ...
4
votes
2answers
75 views

Which forecast way is better

I want to predict daily headcount in a given area. The area can be divided into several blocks. The blocks share very little similarity. The question is, if I'm only interested in total daily ...
4
votes
0answers
104 views

Is the ALRE method of standardization/rescaling appropriate for proportion data?

I have data in which groups of experts make proportion estimates. I've been encouraged to use the ALRE method of scoring the error of these estimates. I found an article which describes this method: ...
4
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0answers
3k views

Hold out sample vs. cross validation for time series, and how to perform in R

I think out-of-sample validation testing for accuracy is essential in initially judging what time-series forecasts to use. In any case, I've been doing some reading on the two most common methods, ...
4
votes
0answers
2k views

VAR model for price forecasting in multiple time-series context. How to get “real figures” as forecasts?

Sorry for the rather long introduction, but since I was (legitimately) critizised for not explaining my cause and questions enough, I will do so now. I would like to conduct a (price)-forecast based ...
4
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0answers
519 views

Time Series: Seasonality and trend

I am interested in financial time series and I have a small question regarding the use of the forecast package. The time series I am interested in is a monthly one and present clear evidences of ...
3
votes
0answers
19 views

Seasonal ARIMA- non stationarity after differencing and seasonal differencing

I am working with a seasonal time series, which is initially stationary. After many attempts, the best model that fits the data is an ARIMA(0,1,4)(0,1,1)[12]. However, checking for the stationarity of ...
3
votes
1answer
77 views

Data leakage when using walk forward optimization

I am setting up a neural network that will predict the incoming customers at a store for the next seven days (the output is a list with seven numbers, one for each day). As input, I will give the ...
3
votes
0answers
63 views

Time series model for demand forecasting?

I have a time series $Y_t$ (example:university applications received in a certain month) which I want to forecast. I have another time series $X_t$ and I know that $Y_t$ is related to past lags of $...
3
votes
0answers
207 views

ARMA process forecasts and maximum likelihood parameters

I have some trouble understanding the forecasting/inference process of ARMA models. From Hamilton (which I am reading now), we can obtain forecasts at $Y$ from any linear process with r.v. values $X$...
3
votes
0answers
70 views

Relationship between Total Over/Under scores and actual total scores in sports

I have a data set of actual scores from sporting games, matched with the bookmaker's Total Over/Under Score (O/U Score) and the odds the bookmaker was offering that the game's total score would fall ...
3
votes
0answers
116 views

Does Nickell bias matter in forecasting?

The context is longitudinal data, with $i$ indexing individuals and $t$ indexing time. The goal is predicting $y_{it}$ as a function of lags of $y$ as well as $\mathbf{X}$, which might include lags. ...

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