Questions tagged [forecasting]

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

1,249 questions with no upvoted or accepted answers
Filter by
Sorted by
Tagged with
2
votes
0answers
74 views

Using maximum of forecasted values to forecast maximum

I am using an algortihm to generate a daily sales Forecast and have concluded that the Forecast is, for pratical purposes, of good enough quality ("low" wMAPE). In general, and without further ...
2
votes
0answers
119 views

How can I normalize truncated variables for a neural network?

Generally, I normalize variables using standard normal variates or (x-xmin)/(xmax-xmin) But this only works well for variables that are not truncated, for example ...
2
votes
2answers
424 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 ...
2
votes
0answers
363 views

Is the forecasting model I am using overfitting and what is the best place to end training?

I am working on a forecasting model for natural gas consumption. I have many exogenous variables and when I train the data with the nnetar model(using R and the forecast packagae), one can specify the ...
2
votes
0answers
270 views

Convergence of predictions of an autoregressive model

I have performed a simple autogregressive model with lag 2 on a time series data. After obtaining the coefficients, I have computed the predictions. Since the lag is 2 in model, the first prediction $\...
2
votes
0answers
193 views

Forecasting autoregressive model. What's the best linear predictor?

Obviously if $X_t = \phi X_{t-1} + Z_t$, then the best linear predictor of $X_t$ given $X_{t-1}$ is $X_t = \phi X_{t-1}$. But if $\phi$ is unknown, one may attempt to substitute $\phi$ by a Yule-...
2
votes
0answers
224 views

Interpreting forecast predictions of log transformed data

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 ...
2
votes
0answers
60 views

Are there any other models besides ARMA models that require stationarity?

Every now and then I come across a discussion of forecasting methods that mentions the topic of stationary time series vaguely without specifying that it is a question mainly in the context of ARMA ...
2
votes
0answers
164 views

How to interpret model confidence set in R

I want to compare 8 different forecast models to each other. Since I dont want to run into the $\alpha$-Inflation of multiple testing I heard about the model confidence set form Hansen. I did this ...
2
votes
1answer
24 views

Parameter estimators of linear predictors

Suppose a linear predictor of the form $a + b'X$. To find estimators for a and b, should we minimize $E[Y-a-b'X]^2$ or $E[(Y-a-b'X)^2|X]$. Former gives $\hat{a} = E[Y] - b'E[X]$ and latter gives $\...
2
votes
0answers
50 views

Computing a corrective regression forecasting factor

I am working on forecasting problem using a regression model like gradient boosting to predict the number of weekly sold shoes. I am using the historical data only from last year to predict the sales ...
2
votes
0answers
93 views

Forecasting turnover on retail industries with time series

I'm working on a project of forecasting turnover on retail industry. I have the turnover of different products for a 2 years period of time My final goal is to forecast a global turnover with and ...
2
votes
0answers
48 views

Evaluating which forecasting method works better? Statistical or Business Forecast

Somewhat new to the forecasting area. I am trying to evaluate whether the statistical forecasts are better than manually generated forecasts in one of our used cases. I have 1000s of customers who ...
2
votes
0answers
158 views

Can i use Diebold Mariano test for comparison of 2 models across multiple time series?

I have 2 models (for simplicity, let's call them AR(1) and MA(1)) each making 1 day ahead forecasts of time series. If I had only 1 time series I would just use ...
2
votes
0answers
36 views

Testing time-series forecasts against actual observations

I'm conducting an event study on annual executive salaries. I have a sample of 52 companies which have been given a cartel fine during year 6 (Event year). For each company, I have a time series of ...
2
votes
1answer
429 views

ARIMA(1,1,1) Model - Forecast

How does one write the mathematical equation for the ARIMA(1,1,1) model with the estimated coefficients below and use the ARIMA(1,1,1) model and time series points below to produce a forecast value ...
2
votes
0answers
188 views

How can a combination of Random Forest and Linear Regression improve a time series forecast?

I attended a presentation by some consultants for retail demand forecasting who showed that for one of their clients, they were able to improve their demand forecasting by replacing a traditional time ...
2
votes
0answers
295 views

How to forecast hierarchical time series with external unique external regressors for each base time series?

I have hierarchical time series with 70 base time series, forming 4 level of hierarchies. I am using forecast() function in R from the package forecast. The ...
2
votes
0answers
47 views

Out-of-sample forecasts: Why does model with log-transformed variables perform so much better?

I am developing a model to forecast the number of students enrolled in roughly 65 primary schools in a large city. Relevant predictors include the number of appropriately aged children living in the ...
2
votes
0answers
125 views

Explaining stationarity in a visual way

I am new to forecasting and want to try and explain to my peers in a visual and simple way how you know if a time series is stationary or not. In the forecasting books I have read, the advice is ...
2
votes
1answer
65 views

Effect of strong auto-correlation on forecasting?

Suppose a wise-sense stationary univariate time series has relatively strong auto-correlation of lag-length of 1, say, around -0.7 Then how would it affect the forecast? Conversely, if a ...
2
votes
0answers
86 views

Assign less weight to most current observations in forecasting

in forecasting, typically, we assign a heavier weight to the most current observations. However, I am finding many cases where a "blip" in last month's sales leads to a very pessimistic view about the ...
2
votes
0answers
206 views

Why does stl() decomposition require integer frequency?

I need to decompose and forecast weekly series with around 10 years of data. In this data leap years play an important role so I need the have non-integer frequency, frequency = (365.25/7) By reading ...
2
votes
0answers
162 views

Are there any rules of thumb for the number of hidden layer neurons in a RNN or LSTM for time series prediction?

Say that I have a univariate time series X(t) that I want to forecast using RNN/LSTM. I have 2 years of weekly sales data that is seasonal. How many hidden layers and neurons in each layer do I need ...
2
votes
0answers
119 views

How is the number of parameters k determined when calculating the AIC of an ARIMA model?

An ARIMA model is specified by 3 parameters $(p,q,d)$ or 6 (+1 for the seasonality) if we consider a seasonal ARIMA model $(p,q,d)(P,Q,D)_s$. The AIC used to select ARIMA models is calculated by: $...
2
votes
0answers
507 views

Accuracy measures in training/test split of time series

I'm using Forecast Principles and Practice 2 to study time series and a doubt came in mind while I was trying to do exercise 7 of chapter 3. How sensitive are the accuracy measures to the training/...
2
votes
0answers
62 views

Forecasting costs with forecast interval using past performance

I'm trying to adopt a model for project cost forecasting in agile. Consider the following table of previous costs per sprint, along with story points completed: ...
2
votes
1answer
123 views

ARIMA forecasting using exogenous variables with their own forecast intervals

Suppose model <- Arima(y , xreg=cbind(x1, x2), order=(p,d,q)) If I am forecasting $x_1$ and $x_2$, then for forecasting $y$: 1) If I use expected forecasts ...
2
votes
0answers
353 views

Is there any interpretation of parameters in Holt Winters method?

I am doing forecast on time series on R and I use exponential smoothing method Holt Winters. Does a value of $\alpha$ close to $0$ or $1$ "mean" something particular about the series? Same question ...
2
votes
0answers
89 views

What machine learning techniques to use to predict for multiple seperate sequences of time-series data?

I am having difficulty structuring my data and finding a machine learning technique to predict my outcome. My data: I have a number of users with observations of a number of factors each year, each ...
2
votes
0answers
982 views

Multiple short multivariate time series forecasting

I have a dataset for a lot of subjects (current testing dataset around 3000 subject, actual number is a lot bigger >40000). Each subject has 13 variables. The data was measured once per year for 11-...
2
votes
0answers
333 views

Building the covariance matrix for hts prediction intervals

In my previous question: Using information about covariance between ARIMA models in forecasting I was interested in the more general case of how to use the covariance matrix in prediction intervals ...
2
votes
0answers
49 views

Neural Networks for predicting Energy at particular date

I am trying to predict Solar Energy value at particular date.So,for this I am applying Artificial Neural Networks model.I am having problem in deciding activation function. Since sigmoid function ...
2
votes
0answers
298 views

Best measure for multiple time series modelling prediction methods?

Newbie question, sorry. I have a highly seasonal monthly time series, predictable with no exogenous/independent variables and no obvious trend. I want to show that a suitable state space model (using <...
2
votes
0answers
33 views
2
votes
0answers
762 views

Demonstrating Overfitting in a Simple Model

I have been working with a finance team to help forecast revenue for some product data. Particularly when the series are short and difficult to forecast, their first response is to add a bunch of "...
2
votes
0answers
570 views

Does seasonal differencing in SARIMA model take care of additive/ multiplicative seasonality?

I am exploring the use of ARIMA and Seasonal ARIMA models (SARIMA). In some of my datasets, I can clearly observe seasonality in the ACF and PACF plots (the lines at seasonal lags clearly cutting the ...
2
votes
0answers
60 views

Forecasting method used for predicting the date of some events

If i'm working in a car company and I have some data for every customer i.e. Their license plate Date of their car's service in the dealer Number of km in their car when they service it Their ...
2
votes
0answers
1k views

Repeated arima forecast returning warning and NA value

I have the code below which trains a model with some predictors, forecasts it one step, appends the forecasted value on the original training data and then tries to feed that back in and train and ...
2
votes
0answers
824 views

Difference between estimation and prediction in simple linear regression model?

Here is what my notes say about estimation and prediction: Estimating the conditional mean We need to estimate the conditional mean $\beta_0+\beta_1x_0$ at a value $x_0$, so we use $\hat{Y_0}=\hat{...
2
votes
0answers
275 views

Why is MASE scaled by the mean absolute error produced by a naive forecast calculated on the in-sample data

Wouldn't a better scaling factor be with the MAE produced by a naive forecast on the test data itself? When evaluating MASE for the training set, this essentially becomes a comparison for the ...
2
votes
1answer
451 views

SAS: Holt Winters Forecasting

If I have an estimate for Holt Winters model as the attached image. How do I interpret the estimates i.e the level, trend and seasonal smoothing weight.
2
votes
1answer
372 views

ARIMA model for vehicle-speed prediction

I am learning on how to predict with ARIMA models. To get some knowledge I read trough some online tutorials for R and ARIMA models. Now I wanted to try this by myself with a problem I am currently ...
2
votes
0answers
317 views

Looking ahead at seasonality in time series modeling without overfitting

In forecasting the performance of many agents in a time series, there is a strong seasonality component, in addition to non-seasonal features for each agent. How can I capture the overall seasonal ...
2
votes
0answers
599 views

Autocorrelation function and forecast in ARIMA model

Let $B$ the lag operator and $\{y_t\}$ the following model $$(1-0.6B^4)y_t=(1+0.2B)\epsilon_t$$ where $\epsilon_t\sim N(0,16)$. a) Is it a stationary process? b) Find the autocorrelation ...
2
votes
1answer
54 views

When will YTD hit a goal?

I'm estimating a deadline, when my time series will add up (total so far) to a certain large number. I'm doing so by getting a forecast line, plus or minus the RMS error of the known values. But if I ...
2
votes
1answer
78 views

How to determine or diagnose that time series data contain seasonality pattern for SARIMA in R by function

I want to ask about seasonal ARIMA (SARIMA) in R function how to determine that time series data has affected or influenced by seasonal pattern Thank you very much
2
votes
0answers
53 views

What model should I use for retirement forecasting?

I am a HR professional looking to self learn statistical modeling for new responsibilities at work. I need to forecast no. of employees who may retire next 10 years. What would be simple way to ...
2
votes
1answer
65 views

building and analyzing a regression model

I'm trying build a model to predict sells of clothe store for each cluster to month 11 and 12. I've 98 stores, and for each store i have this data, but i put the all data to calc only 1 model. I use ...
2
votes
0answers
155 views

How to compare the forecast accuracy of two models when the data has unbalanced panel structure?

I am comparing the earnings forecast accuracy of two models (model 1 and model 2). The data (firm-year level data) have an unbalanced panel structure since firms have varying lifetime and time to be ...

1 2 3
4
5
25