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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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Can I use VAR model on I(1) series with cointegration?

I have four I(1) series, and the Johansen test(ca.jo()) shows there is one cointegration. My purpose is to forecast, so I want to compare the forecasting results of VAR and VECM model. Is this ...
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Simulating AR/MA Process and forecasting it [on hold]

I'm trying to simulate an AR and a MA process separately. I found the ACF and PACF as well. If now I want to forecast for one time period ahead, can I do that with a simulated time series in R, if ...
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Multilevel modeling: many groups, mostly one sample each

I have a modeling problem in which customers can have multiple accounts on a website. Roughly speaking, 50% of customers have one account, 25% have two accounts, 12.5% have three accounts, etc. There ...
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How to build forecast model [on hold]

I have some data about budget overrun and fee erosion. I have determined that greater the budget overrun, greater the fee erosion. Conversly, lesser the budget overrun, lesser the fee erosion. I ...
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Upwardly biased forecast results due to period of high demand: how to deal with this?

I'm currently working on a call-center forecasting project with some data limitations. Currently it is still a learning-project, and I started with simple OLS regressions. For the months 2016-12 to ...
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Different set of predictors significant for different sample sizes - how to interpret results?

So I am trying a GARCH framework with external regressor(s) to predict returns. The external regressor, $y$, intuitively has useful lags that could predict the response. I'm slowly accumulating data ...
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Converting log transformed and differenced time series back into original in R

I have built a Garch model in R based on taking a log transformation and a one order difference on the original time series. I would like to know how develop a forecast based on the Garch model for ...
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1answer
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How to calculate mean directional accuracy in R [on hold]

I have been searching for a method to calculate the mean directional accuracy in R. Basically I have a few forecasting arima models, and I would like to choose the best model. Besides looking at the ...
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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. ...
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How to add the effect of structual change points (level shift, local time trends, changes in seasonal pulses ) in ARIMA IN PYTHON?

I am working on a time series forecasting problem which is described in details here. As I came to know that I was not considering structural changes and seasonal dummies and was building a simple ...
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Time series forecasting using Neural Network in matlab [on hold]

Please help me to create neural network to forecast inflation in Matlab for my research.
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Automating data preparation and model fit steps for multiple products

My question is in continuation to the these questions: Question 1 Question 2 Question 3 Question 4 I am also attempting to automate the forecasting of financial parameters of multiple products in the ...
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How to do out of sample forecast by SVM in r? [on hold]

I am doing uni variate forecasting by using SVM in r. I did my in sample forecast precisely but when i do forecast for some next time period it gives the same values. here are codes. ...
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Model for forecasting daily page views of a web page in R

I have to forecast daily page views of a web portal. We have the daily page views of the data for the last 2 years. We have to forecast for next 90 days. I am using a multi-seasonal (with season ...
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1answer
127 views

Time series forecast - ARIMA/ARIMAX with daily data [closed]

I am working on a project to analyse and forecast time series for sales and revenue of a client. There are various models that i want to test for accuracy purposes - namely Holt Linear Method, Holt ...
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1answer
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What does it say about your underlying system when the granular level is not predictable, but aggregations are?

This might be too "discussion-y" rather than black-or-white-answer for here, but thought I'd try. If there's a better direction someone can point me, that's fine. Example: One McDonald's store daily ...
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Intra-Day Time Series Forecasting w/ non-continuous data

I have 8 sample frames taken at the end of each quarter starting in 2017Q2 and ending in 2019Q1 for a stock ticker. Each sample frame consists of 2 trading days worth of stock data (open, high, low, ...
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1answer
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Contemporary term for setting of forecasting constants

Two time-series forecast principles are used: a seasonal moving average and a seasonal single exponential smoothing forecast. To attain quality results, the associated constants (the season size, the ...
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Forecasting Methodology

Suppose I have only 1 variable (data on export, monthly, non-seasonally adjusted) from Jan 1960 till Mar 2019. My task is to obtain forecasts of this series for the coming year (i.e. Apr 2019 - Mar ...
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How do I specify a moving average model in R-INLA?

I have a dynamic regression model specified as follows: $f_{c,t+1} \sim N(\eta_{c,t+1} + \phi\epsilon_t,\sigma^2_{f})$ $\epsilon_t=f_{c,t}-\eta_{c,t}$ $\eta_{c,t}=\beta_0+\beta_1x_{1,c,t}$ How ...
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Calculating probability of a female US president [closed]

What solid evidence could we use to assess the chances of a woman being elected in the next US presidential election 2020? The last 45 US presidents were male. That's a hit rate of 100%. Intuitively ...
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Formulae for density function replacing probabilistic function

I am working with markov chains and in specific I am working with (Langrock, Roland MacDonald, Iain L. Zucchini, W (2016)). They define a forecast distribution given as: $$ Pr(X_{T+h}=x|X^{(T)}=x^{(...
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Decide inventory to carry based on forecast model to max. profit

This is statistical modeling / forecasting optimization question. I have a forecast model which predicts units value for a year. Now if I carry more inventory than prediction, I lose all the extra ...
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1answer
22 views

Calculating forecast intervals for user adjusted forecasts?

My demand forecasting solution generates point forecasts and forecast intervals for millions of time series (# of products × # stores for a large retailer) using statistical models. Business users (...
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1answer
28 views

Forecasting a time series without trend and seasonality?

Can I use ARIMA methods to forecast a time series without any trend or seasonality? If no, are there any other methods that I can use to do such forecasting?
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Random Forest for time series - how to deal with mixed frequencies?

I want to use random forest to forecast a quarterly sampled variable $Y_t$ using a large set of monthly sampled regressors $X_t$. The point is that I have very few observations (I'm working with ...
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1answer
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ARMAX for Bitcoin prediction via sentiment

I am looking for correlation between social sentiment of BTC and the financial value of BTC. My approach is to use a time-series model to predict the bitcoin value at t+1 based on multiple input ...
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Prediction of financial time series

I have several questions. I will split the text up in one high-level description of the goal of my exercise, a detailed description of my potential solution and finally my actual questions. Please ...
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Long fitting times for SARIMAX. Fine tuning required for hourly data with weekly seasinality

I have developed a SARIMAX model for hourly data of two months aprox 1700 records and predecting a week data around 170 records. As the seasonality observed is for a week so m=168. Initially used GRID ...
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32 views

Using temperature as feature in Neural Network

I'm currently putting together a Neural Network for doing Sales Forecasting for hundreds of products. The domain experts know that the sales spike when the temperature drops and so I started to use ...
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22 views

What does this mean? Can you explain it to me in laymans term? [duplicate]

How to interpret this? What does the ARIMA(0,0,0)(0,1,0)[12] means in laymans term?
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Distance from expected error versus distance from expectation

Model 1: percentage from expectation We have a time series forecasting model that makes predictions every 15 minutes. As a first stab, we classified all those points that were 50% larger than our ...
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The Efficient Market Hypothesis and forecastability?

According to Wikipedia: The efficient-market hypothesis (EMH) is a theory in financial economics that states that asset prices fully reflect all available information. A direct implication is that ...
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Forecasting many points (horizson forecast) VS rolling one by one

I used a Feed Forward (or Convolution or RNN) for forecasting and I want to forecast many steps. I have to options: To forecast many points simultaneously. Each time to forecast just one point and ...
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1answer
33 views

ARIMA model forecast does't seem right

I am doing a SARIMA forecasting for my monthly data in STATA, and below is my forecast. I use a SARIMA (1,1,1)(0,1,1,12) model, but the forecast seem to only capture the previous month pattern, which ...
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1answer
24 views

Manually calculate SARIMAX forecast

I'm trying to manually replicate the forecast that I obtained using statsmodels.api sarimax (python). Its actually just an AR(1) model with one exogenous variable, in the form of SARIMAX(1,0,0)(0,0,0)...
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1answer
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Is statistical significance of a regressor important in forecasting different scenarios?

I have a good regression with ARMA errors (with exogenous variables), and it gives me reasonable forecasts. But now I need to construct different scenarios to forecast. My question is: Should I worry ...
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1answer
180 views

Can I use averages to improve the forecast of a multiple regression?

I have a cross-sectional multiple regression that I have estimated and now I would like to apply it to make a simple forecast of the dependent variable. Take the data generating process $$y_i =\...
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Time shifting in time series forecasting

I am working on an ANN model for univariate time series forecasting. The step size is 1, so I try to forecast value of t+1, using value of t. Unfortunately, my forecasts have time shift problem. The ...
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Method of analyze returns for nonstationary time-series

guys. Let's have 2 types of time series: TrendStationary (or TS-stationary) and DifferenceStationary (or DS-stationary) time series. So, what's the best strategy of analyzing and forecasting time ...
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Interpreting accuracy values in ARIMA

How to interpet each measuring accuracy and how will i know if its accurate measure in the model
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1answer
62 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, ...
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Time-series forecasting of weakly correlated, univariate series

Given an event that happens with a probability of $\lambda_1$, and another event happens with a probability of $\lambda_2$, what is the probability that they both occur? I have a dataset of ...
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non-stationary time series for VAR model forecasting

I'm working with a VAR model to do forecast involving two non-stationary time series (quarterly frequency). The literature indicates to verify if there is cointegration and, otherwise, to use the ...
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1answer
42 views

How does facebook prophet handels missing data?

Prophet's paper (forecasting at scale by SJ Taylor - 2017) says the following on missing data: "Unlike ARIMA models, the measurments do not need to be regularly spaced, and we do not need to ...
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2answers
58 views

Does SARIMA(3,1,18)(8,1,3) exist?

When I entered the above model in minitab to forecast, it said, 18 is not acceptable, and that value should be less than or equal to 5. I wonder whether it's a limitation of minitab, or this model is ...
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1answer
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Interpretation of spectral entropy of a timeseries

The tsfeatures package for R has an entropy() function. The vignette for the package describes it as: The spectral entropy is ...
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1answer
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Forecasting in R - really short time-series

Complete noob on forecasting and time-series here. I'm doing my PhD and my group did previous research on the prevalence of the disease we're studying. We only have prevalence data of 5 years and ...
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Forecasting data for 5 years [duplicate]

Following is a part of my data-set. What are the tests I can use for it? Can I use any forecasting method? If yes, what are those methods?
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I need help with choosing a mid-long term forecastic method for this demand

I am trying to forecast the demand of a product for the next 36 months, based on its sales history. The demand plot is shown below. I honestly don't know what to do with it. I tried linear and non-...