Forecasting involves estimating the value or distribution of a random variable which has not yet been observed.

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Forecasting - Population Death Rates

Wondering if you can help me out with this problem: I have 2 closed populations of products (call it Product X, and Product Y). Population Size of each product 10 million each (Product X = 10 ...
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12 views

modeling sales based on different quantitative and qualitative variables

I'm new to machine learning and am interested in being able to try different modeling approaches to find the model that will predict future sales the best without overfitting/overtraining. I have been ...
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12 views

Confidence intervals for Tobit model in package AER in R

I use a Tobit model to predict censored data. I use the AER package in R. A toy example looks as follows: ...
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23 views

Combining Forecasts: Best Information to Solicit from Forecasters?

Suppose Statistician $m=1$ produces a set of $h$-step-ahead point forecasts $\hat{x}_{t+h|t, 1}$ of $x_{t+h}$ where $x_{t+h} \in [0,1]$. Also, this point forecast could come with: a predictive ...
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Constant-output Markov chain in time-series prediction

Suppose a Markov chain with two discrete states $A$ and $B$. The probability of moving from $A$ to $B$ is $0.1$ and the probability of moving from $A$ to $A$ is $0.9$. Similarly, $B$ to $B$ has ...
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How are Markov chains used for time-series forecasting?

How are Markov chains used for time-series forecasting? Since the next state depends only on the current state, I would guess that I should first find the steady-state probabilities. To predict a ...
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42 views

How to interpret and do forecasting using tsoutliers package and auto.arima

I have got monthly data from 1993 to 2015 and would like to do forecasting on these data. I used tsoutliers package to detect the outliers, but I do not know how do I continue to forecast with my set ...
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32 views

Pros&Cons of Hidden Markov Models in Time Series Forecasting

What are the advantages and disadvantages of Hidden Markov Models in forecasting values of a time series (compared to other methods, e.g. ARIMA)?
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31 views

Fitting ARIMA+GARCH in R [closed]

I have a line of code in R: ...
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40 views

Should I ignore negative prediction values?

I have the following time series of count data: ...
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29 views

Double-Seasonal Holt-Winters forecasts for periods where $k>s_1$

I have the feeling that this may be a stupid question, so apologies in advance. I have a set of daily demand data (midnight-midnight) which clearly has two very strong seasonal cycles - one ...
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13 views

Project management use case - how to predict resource allocation

I have a historical data set with project information including things like duration of project, number of resources per week, number of hours worked per resource per week. I was wondering if there's ...
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20 views

measuring accuracy in one step forecast(using auto.arima() and ets() in R

I’m working on workers’ remittances data (quarterly) for Bangladesh. The data span is from 1980 quarter 1 to 2014 quarter 4. My objective is to do univariate time series forecasting with ...
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10 views

how forecast with penalized b-spline

I have inflasi data, and i will forecast future with penalized b-spline, but I have problem after having found lambda. Do you know syntax to uose in SAS for forecasting values from a penalized ...
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15 views

10-fold cross validation for forecasting time series with explanatory data ?

I saw that the question was asked some years ago here, but I wasn't satisfied with the answers so I'm asking it again. Is there some theoretical foundations about not doing k-fold cross validation ...
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17 views

How to predict or forecast in transfer function model?

Is there any way to predict a transfer function model by using predict() (or else) in R? I'm searching the package to do forecasting in transfer function model, but couldn't find one.
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Quantile regression for Sales forecast in R

Issue: Cannot forecast sales accurately using quantile regression in R. I am using rq function from "quantreg" package which is giving me warning "Result might have Non unique solutions" Aim: I am ...
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46 views

Improve ARIMAX model, compared to arima model

I am trying to model an ARIMAX model on my time series. ...
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14 views

Error of long-term volatility forecast

I am not a specialist statistician (although many years ago I did study maths at university). I am trying to calculate the error of a long-term volatility forecast of a time series and have got a bit ...
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24 views

Getting lagged values of indep. variables to model contemporaneous values of the dep. variable

I am trying to forecast the variable, oenb_dependent: My current sample data looks like that: ...
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37 views

What is the correct unit root/stationarity test for this variable? Why do different tests provide different conclusions?

This is something of a follow up question to a previous question I had here: Can over differencing cause a singular matrix in a VAR model? A brief recap of what I am trying to accomplish: I want to ...
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23 views

Forecasting bank account balance data

I'm new to statistics and not even entirely sure how to frame my question properly, so please go easy with me. I would like to be able to do 2 things: Take a series of dates and bank account ...
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2answers
42 views

Number of Days in a Monthly Forecast

So for the last few months I've been doing a lot of forecasting for my company and specifically I've been looking at monthly forecasts of total weight of different categories of products output's each ...
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24 views

Can over differencing cause a singular matrix in a VAR model?

I am using a VAR model to forecast employment in a city. I have 2nd differenced all of my variables and checked for unit roots via ADF tests. When running a VAR model on this data through R, I am ...
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+50

How to train radial basis function for function approximation?

There is an Autoregressive model of order 1 (AR(1)) that is excited by a non-linear signal as the input: $$x_t = \rho x_{t-1} + u_t \tag{1}$$ The time series $u_t$ is generated from a Mackey-Glass ...
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91 views

Prediction intervals for forecasts using spectral analysis

I have circadian data which typically have a period of around 24 hours so using spectral analysis seems appropriate. I've used spectrum resampling which is quite robust to changes in period which ...
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63 views

Best way to deal with forecasting with noisy data?

I have a bunch of sales data. It is from distributors of 2000 different items, who service big companies and large distributors to a number of small independent stores. They sell some items which do ...
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21 views

Demand bottom-up forecasting and substitution effect

If retailer has many products the is likely to be a substitution effect within product groups (clusters). Hence, there is a notion of the "unit of demand" that is supposed to gather products based ...
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Forecasting demand with out-of-stock data

Usually retailers have a service level that is below 1.0, which means that share of products is out-of-stock some of the time. What is the best practice or possible ways of using out-of-stock data to ...
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43 views

Density forecasting

I am having some troubles obtaining density forecasts of any returns series. As I couldn't find any numerical examples on the Internet, I would like to ask you guys for some help. My goal is to ...
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23 views

Calculating the optimal Holt Winters parameters (not in R)?

The Holt Winters (HW) technique requires the following parameters: Alpha, Beta and Gamma. The accuracy of the forecasts depends on these parameters. Some software packages (like in R) are able to find ...
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How do I Forecast new Yts given new Xt's using a Dynamic Linear Model?

I am trying to forecast predict new observations of interest rates given new data using the DLM modeling framework. Essentially, my problem is this: I have a training set (a set of data i want to ...
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50 views

Does ARIMA require normally distributed data? [duplicate]

I want forecast inflation using ARIMA model. My questions are: Does ARIMA require normally distributed input data? (Because my data—inflation—is not normal.) If ARIMA require normally ...
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29 views

What are some tests for the predictability of time-series?

I have 2500 time series which I want to test the predictability and based on that, choose the best one to forecast. Ideally I want to use a simple model like ARMA-GARCH for forecasting. Are there ...
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33 views

Multi-step ahead forecasting with Weighted Moving Average?

The Weighted Moving Average method is usually used for smoothing purposes. However, it can be used to forecast $Y(t+1)$ based on the last n observed data. In real-world problems, forecasting in very ...
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auto.arima and DLM give different values for loglikelihood

I want to estimate an ARIMA model on my timeseries, then represent it in state space format, mainly because it will be more responsive to change in pattern. I used ...
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36 views

Why no MAX models?

I'm diving into the field of system identification, black box modeling and forecasting. A lot still has to become clear to me, but one question that came to my mind (and to which the answer might ...
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2answers
24 views

Forecasting methods for monthly sales

I have to make predictions about sales on a monthly base and I already have historical data from January 2011 until June 2015. What forecasting method should I use if my data is influenced by ...
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1answer
97 views

Why can't my (auto.)arima-model forecast my time series?

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 ...
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Forecasting using polynomial function of R and how to validate with available methods such as ETS

Trying to build a forecasting function to model a polynomial function to fit the time series data and to use the same to predict the future periods and i have used the concepts of lag as the predictor ...
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23 views

Nonlinear forecasting

I'm working with time series data (which fluctuates constantly) and currently have 27 data points to forecast with. Would anyone be able to recommend a nonlinear forecasting method using formulas to ...
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62 views

Why can't we use top-down methods in forecasting grouped time series?

As I asked in here I was trying to forecast grouped time series with two grouping variables and I find some limitation of hierarchical forecasting methods. In particular, using hts package from R, we ...
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40 views

Constant in arima model whether to include or exclude?

I have a very basic question on including constant in Arima models. I'll illustrate this by an example. I have the following ACF and PACF of a weekly time series that is differenced at lag 1 (trend) ...
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39 views

Work with results of tbats decomposition

I made a time series decomposition with tbats. There is weekly and yearly seasonality in the data (and maybe also monthly - not really important for the question) ...
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33 views

Online time series forecasting with DLM

I have estimated a univariate time series model, consisting of a random walk and an AR component. Now the goal is to make forecast about a couple of steps ahead as new data comes in, in an online ...
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How to forecast how many passenger will be coming or be lost if a new departure time added or a current one removed?

I want to forecast how many passenger will be coming or be lost if a new departure time added or removed from time table based on historical data? I have historical data from 2010 to 2015 on how many ...
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23 views

Best forecast method for my data

I have a large amount of statistical data on tennis matches over the last 10 years and want to be able to forecast the percentage of points a server will win on his own serve based on past data. For ...
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1answer
43 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 ...
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25 views

DLM, regression and multiple time series

I'm working with incoming traffic data at multiple spots along a long road. Let's denote the traffic at time t at point $j$ by $x_j(t)$. For each spot, a univariate model, such as local level plus ...
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2answers
78 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 ...