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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32 views

Time series when seasonality appear due to both solar and the lunar calendars

I have a time series data as shown in the figure below where the X axis is the serial number of the day of the year form 1 to 365 where 1 is 1-Jan and 365 or 366 is 31-Dec. The Y axis represents the ...
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32 views

Quantiles of transformed series

Let's have a time series $S_{n}$ of a same market asset. Let's $R_n = ln(S_n/S_{n-1})$ be an asset returns. So, I could forecast same $\overline{R}_{N+1}, \overline{R}_{N+2}, ... , \overline{R}_{N+j}$,...
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19 views

Product Demand Forecasting for Mutliple Products in Single Warehouse

I am working on a new project I haven't much experience with and was looking for insight on where to begin and methods to use. I am trying to produce a demand forecasting model (or perhaps sales ...
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1answer
199 views

Predictions remain same for ARIMA model?

I have a table which has data CO2 emission of the world from 1960 to 2011. After going through some tutorial i performed ARIMA method on my dataset,but the prediction of CO2 emission for the next 10 ...
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1answer
139 views

In prediction, when should I use rolling windows vs. nonoverlapping ones?

Suppose I have daily time series data and I want to predict a month in advance using a set of features. I have lots of them so I'll be using regularized linear regression. To create the response I can ...
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1answer
26 views

Aggregating sales forecasts from mutually exclusive segments

I am using generalized additive models (GAMs) to forecast sales for 16 mutually exclusive and exhaustive customer segments. There are naturally correlations in these 16 series, including seasonal ...
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3answers
557 views

Capture seasonality - ARIMA in R

I have a time series Y, for one year and measures taken every 15 minutes. The data show clear seasonality, both daily and weekly. I would like to see the seasonality in the models. I tried various ...
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1answer
90 views

Forecasting Question [closed]

I am looking through Rob J Hyndman's text of forecasting here In line 258, I am getting this Error Message: Error in forecast::autolayer(f.arima, PI = FALSE, series = "ets") : object 'f.arima' not ...
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645 views

How can I forecast a time series using Cart models?

I'm using the rpart library to try forecasting the electricity consumption from Australia (example from the book Introductory Time Series with R): ...
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1answer
61 views

Why naive (prediction) forecasting is called random walk?

Why naive (prediction) forecasting is called a random walk? Naive prediction is to use the last value as a forecast. (It's clear that the best prediction for a random walk is a naive one.)
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1answer
328 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.
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19 views

Forecasting a not-seasonal time series in R

I would to forecast a not-seasonal time serie in R. This is my serie and the model built with HoltWinters: ...
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2answers
186 views

Will ARIMAX or exponential smoothing forecast a short time series better?

The objective requires to predict GROSS NPA for 6 months and provided with 2 years of data i.e., around 24 observations. So, which of the method will provide better forecast?
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42 views

How do you forecast a time series that is inherently uncertain?

Most time series forecasting models take in fixed (so presumably deterministic) historical values, and then output either: A point (usually mean) forecast + forecast intervals, so $\hat{Y}_{t+1} = ...
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1answer
57 views

Out-of-sample Rolling window forecast with ARIMA(0,0,0) with non-zero mean

I am doing a rolling window out-of-sample forecast and have fitted an ARIMA(0,0,1) model to a first difference time series. People argue that sometimes simpler models are better than more complicated ...
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1answer
30 views

Auto-regressive time series prediction

$\hat{x}_{t+1} = \beta_1x_t + \beta_2x_{t-1} + \dots \beta_Mx_{t-M+1}$ So we actually base our prediction on the previous $M$ values. The task then specifies that we need to minimize the objective ...
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17 views

Vector autoregression with restrictions

lets assume I want to forecast sales, market and share of market (sales/market) of a product. By forecasting sales and market separately I could imagine, that I got a unrealistic forecast of the share ...
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3answers
122 views

Forecast accuracy rolling window

What is the best way to get a measure of how well an ARIMA model can predict a timeseries when doing an out-of-sample rolling window? I cant use MPE cause it contains zeroes. What I am looking for is ...
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1answer
23 views

Tentative ARIMA models for forecasting

I am doing out-of-sample forecasting with ARIMA and derived one model (0,0,1) with auto.arima on a differenced time series. The series is daily observations over the course of 3 years. I would like to ...
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1k views

One-Step Ahead Forecast [closed]

Could someone please confirm for me that the "Example Code" below is a one-step ahead forecast? The reason I ask is because I'm a little confused by the out-of-sample forecast in the Rob Hyndman blog ...
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1answer
30 views

Possible to predict independent variable not among predictors in a vector autoregressive model (VAR)

I have developed a LSTM NN where I predict a variable multiple steps ahead. For this model, the independent variable itself is not among the predictors. I want to compare it with the results of a VAR ...
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19 views

Comparison of ARIMA and VAR accuracy

Can someone help explaining how to compare a forecast from an ARIMA model and a VAR model. I have tried calculating MAPE, MSE, RMSE etc. for my VAR forecast, but i simply cannot get it to work. ...
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14 views

What is the resulting distribution of a data set that was originally normally distributed but has been quantized and had all negative values removed?

I am trying to benchmark a seasonal forecasting model and calculate not just the point forecasts but the forecast densities from the model. To do this, I generated a simulated data set in the ...
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13 views

Unsure how to continuously train a churn model after my model has gone live

I'm having trouble describing this properly so I'll provide as many details as possible. First, here are a few details on the model that I am building: I have built a classification model that ...
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0answers
233 views

How to extrapolate future probability density functions if you have a time series of them as input?

This is my current situation: I am given an observations vector $\textbf{X}$ of continuous variables with a time component $T$ (not equallly distanced). My supervisor approximates densities with ...
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1answer
23 views

Are the forecasting methods like mean, naive, drift, weighted average applicable to non stationary time series?

Like AR, MA models essentially need the series to be stationary, do the other forecast methods mentioned above also follow stationary?
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2k views

No suitable ARIMA model found error

I am trying to forecast the sales for next 48 days from the data given by modelling for multiple seasonality and day of week , promotional effects. R could not come up with a suitable model. I need ...
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3answers
47 views

What methods of forecasting should I be looking at to forecast sales?

I am wanting to forecast sales of different products within a business. I have a good background in mathematics (but mainly focused on analysis, group theory, algebra etc. as opposed to statistics). ...
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2answers
84 views

Why TBATS model giving poor result?

I have time series data of number of units ordered from a manufacturing plant and number of units delivered. The are multiple different plant sites for which I need to build forecasting models. I ...
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1answer
27 views

Training model vs model on whole data in time series forecasting in r

I have daily time series data of almost two years starting for Jan 2018 to Nov 2019 and need to forecast for next two months Dec and Jan. My train data(Jan 2018-Aug 2019)is up to Aug 2019 and its ...
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1answer
31 views

Does it make sense to fit an ARIMA model to the remainder component of a timeseries?

Suppose I have a timeseries, something like this: ...
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1answer
148 views

Time Series Forecasting - Daily data

I'm relatively new to time series forecasting. I've been assigned with the task of forecasting operation time of an industrial equipment based on a daily data (3 years of daily data). The prediction ...
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1answer
29 views

Handling zero sales on public holidays when forecasting daily data

I have daily sales data for about 3 years, and I need to forecast the next month. However, zero sales occur during public holidays and weekends. I can easily remove the weekends, so that I have a ...
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21 views

How to figure out which features indicate similarity between time series?

I have a data set with the following data: A large number of time series of sales for various products. A large set (~100) of static features that describe each of these products (e.g. product ...
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1answer
42 views

How to calculate pinball loss for quantiles and for point forecasts?

I have a few general questions about pinball loss: Is a pinball loss typically calculated for each point in the forecast horizon or is it calculated across all points in the forecast horizon? How is ...
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0answers
106 views

Critical or acceptable value of forecasting error

I'm new in R and data mining. I have question about critical or acceptable value of forecasting error. I created 30 days forecast of the parameters for working powered roof support in mine (very ...
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1answer
55 views

forecast rainfall using ARIMA in R

I am a new student approaching ARIMA prediction analysis in R. If the question is too simple or incorrect, please forgive and guide me. I am currently using the ARIMA provided in R. I use the data as ...
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1answer
35 views

How to work with multi-step forecasting on differenced time series

I have a financial time series that I wish to make 5 step ahead (t+5) forecasts on. As the series is non-stationary, I have differenced the series. For every time step t, the response variable is ...
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2answers
16k views

ARIMA forecast with seasonality and trend, strange result

as I am stepping into forecasting with ARIMA models, I am trying to understand how I can improve a forecast based on ARIMA fit with seasonality and drift. My data is the following time series ( over ...
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1answer
122 views

Predictive density and likelihood evaluation at time t+1 of GARCH model

I am new to forecast and I would appreciate any help. I want to do Bayesian estimation of GARCH models. I read a similar question here, but I have some additional questions. The model is $$y_i=\...
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1answer
73 views

Help me about using ARIMA forecasting rainfall [closed]

I am currently using the ARIMA provided in R. I use the data as the rainfall time series in QuyNhon (Vietnam) from 2000 to 2017 to forecast rainfall for the next several years. I wish that the ...
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0answers
64 views

Still seeing seasonal pattern in ACF plot after multiple differencing

I've seen a couple of questions asked here about seasonality "left over" (so to speak) after differencing like this and this, but unfortunately those answers don't help my situation. I have ~ 17000 ...
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1answer
8k views

Why use differencing and Box-Cox in time series?

Why use Differencing and Box-Cox transformation in a time series? From what I read the usefulness of the procedures are Differencing: Making a time series stationary and stabilize the mean Box-Cox: ...
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23 views

Negative coefficients of regressors in arimax,should be positive

I have two years of daily time series inbound call centers data starting from Jan 2018 to Nov 2019. I am doing arimax and regressors are mainly promotional flag along with day of the week (sun, mon ......
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1answer
137 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. ...
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1answer
181 views

Forecasting with AR(1) and pseudo out-of-sample using R

I'm trying to do Pseudo out-of-sample forecasting using R. And, I also have the following initial data (gdp) ...
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0answers
7 views

Is it possible to test variable paths in a VAR model?

I am trying to write a model to predict the GDP. For now I am running a very simple model with the interest rate and the GDP. Since both are correlated, I tried a VAR model. The following code is in ...
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18 views

Scenario Analysis with a GARCH model - conditional forecasts with hard restrictions on dependent variables?

Waggoner and Zha (1999), see reference below, developed an approach to produce conditional forecasts for VAR models with hard restrictions on the variables using Gibbs Sampling. As an example, they ...
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45 views

Is stationarity a requirement when using neural networks for time series forecasting?

I'm getting conflicting information on whether stationarity is a requirement when using neural networks for time series forecasting: In this lecture, the speaker says it isn't a requirement. In this ...
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2answers
120 views

How to predict time series data with ARIMA

I'm a beginner in data analysis field and I wanna try to predict time series data using ARIMA model but I still don't understand some of the concept. I've read some papers about ARIMA and this model -...