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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Appropriateness of time series regression for intensive longitudinal data

I am analyzing time series data in which participants rated their thoughts in real time. I am trying to model the shape of the data. Details on the time series: Sampling rate was 1/4 second ...
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129 views

LASSO: k-fold Cross Validation of AR(p)-process

To improve my intuition on shrinkage models, I want to "recode" the lasso by myself. However, I'm at the point, where I have to program the k-fold Cross-Validation. At my future application of the ...
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1answer
379 views

Trending time series data normalization for Deep Learning

I'm replicating following article Financial Time Series Prediction using Deep Learning and I'm stuck with data normalization. In chapter 5.1 in the second paragraph in the last sentense the authors ...
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242 views

Assumptions on Neural Networks (NNETAR)

Are there any assumptions that must be covered when fitting an NNETAR model? non-correlation, normality, or something? I've already saw Rob Hyndman post where he says NNETAR doesn´t need stationarity, ...
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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 ...
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1answer
3k views

forecasting multivariate time series (with categorical variables) in R

I want to forecast future(next 20 days) sales with sample dataset. This is just a sample data and the actual data is from Jan 2014 to Dec 2016. As you can see, sales tend to increase as time goes by, ...
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382 views

Back-forecasting in MA(2) model

The sales of a certain product are represented by the model $$Z_t=3+a_t+0.5a_{t-1}-0.25a_{t-2}$$ where $a_t\sim WN(0,4)$ (White Noise). Given the data $Z_1=3.25,Z_2=4.75,Z_3=2.25$ and $Z_4=...
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1answer
343 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.
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175 views

How to interpreate Reliability Diagrams?

In general the interpretation of reliability diagrams isn't a problem for me, to identify if a forecast is over- or underestimated. But in this dataset which contains different product probabilities ...
3
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1answer
71 views

How to account for remainder in forecasting?

I've done an STL decomposition of a time series. While trend accounts for major variations, remainder has a "cyclical" setup. I want to know what other forecasting methods I can apply to maximize ...
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0answers
134 views

Steps to find optimal transformation for wide-sense stationarity

I've been trying to automate the procedure of choosing the best transformation for a non-stationary process (in R). For lack of a better term, "best transformation" here refers to the quality of ...
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0answers
60 views

Is 5 in 100,000 the same as 50 in 1000,000 from a Bayesian perspective?

I am learning about bayesian reasoning and I was having a discussion with a friend about it. My friend asked me this question, which I couldn't answer (this is not a homework, I am an adult learning ...
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43 views

How to include handle days with no delivery?

I am currently trying to forecast the delivery of cash to branches. The problem I face is that there are a few days, as well as most Sundays, in which there are no deliveries. Hence, in the time ...
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0answers
109 views

AIC comparison vs. unit root test for model selection in forecasting

Rob J. Hyndman once wrote in "Why I don't like statistical tests" (emphasis is mine): In forecasting, the only place in which I find testing useful is in determining the order of integration of a ...
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0answers
1k views

How to estimate weekly and daily seasonality for data with 15min frequency in Python?

I am relatively new to time series. My goal is to predict a few hours of data, measured every 15min based on three months of observations in Python. I assume I have daily and weekly cycles which I ...
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1answer
104 views

Forecast a staircase-like time series

I am working on this problem for my research. The attached time series represents the memory usage of an application over time. As you can imagine, the memory usage steps up randomly every few days. ...
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0answers
123 views

Holt-Winters: Can I use more than one seasonal cycle for SSE minimisation?

I am minimising SSE to estimate the parameters and starting values for a Holt-Winters model. I.e. "forecasting" the values using different parameters, measuring the sum of squared errors of these "...
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0answers
99 views

Is there a general and completely automated (i.e. programmed) solution to modelling and forecasting time series data?

Is there a general and completely automated solution to modelling and forecasting time series data? I think this question is extremely important. If it is not possible, please provide an ...
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24 views

How to make shrinked forecast for the extreme value?

Let me use made-up example: John loves running. He decided to run in his local half-marathon for the first time in his life. He never measured exactly how fast he runs the distance, but while ...
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162 views

VAR model with different time period for each series

I am trying to fit a Vector Autoregression model to forecast GDP growth Rate. I have 2 series, monthly GDP growth rate and a monthly economic indicator. For the monthly GDP growth rate, the latest ...
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807 views

How to select the length of a time series when fitting models for prediction

Let say that one wants to fit a model to a daily financial time series for prediction (e.g. ARIMA, SVM). If data are stationary, ideally the longer the time series, the better. In practice, I don't ...
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117 views

Comparing variances of forecast errors

I am forecasting a weekly commodity price series. I use a rolling window for estimating my model, and from each window I make point forecasts for one and two steps ahead. I want to investigate ...
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526 views

What's an example of a best linear predictor that's not a best predictor?

I just learned about the definition of a best linear predictor found by minimization of variance of $Y$ given $X$, or in other words trying to minimize the variance of $$\mathbb{E}\left(\{Y-\hat{Y}(...
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270 views

Computing Seasonality Index and application to shorter time series

Background: I have an overall time series of close to 3 years of data. I need to forecast for different slices of data. When I slice the data, some slices results in a shorter time series. We go with ...
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327 views

How to calculate prediction intervals based on Chebyshev inequality?

I have recently read the article by Gardner (1988) who proposes Chebyshev inequality-based prediction intervals for forecast: suppose we have a model selected on the usual basis of one-step-ahead ...
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263 views

is it sensible to use monte carlo to predict sum of time-series over an interval?

I have created a model that forecasts out a time series at the daily level along with prediction intervals two months into the future. There is little to no auto-correlation in the time series so I ...
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2answers
123 views

Evaluating probabilistic forecasts of K-most-likely events from an arbitrarily large event space

Suppose a populous nation has a high homicide rate and an understaffed police force. The police chief hires a statistician and together they decide to take a preventative approach by identifying ...
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178 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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0answers
218 views

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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153 views

Reasons for GLM ('identity') performing better than GLM ('gamma') for predicting a gamma distributed variable?

I am investigating different methods for fitting my target variable (observed wind speed: positive, real, with small values being most probable) using generalized linear modeling (GLM) and - in a ...
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142 views

Seasonal ARIMA Forecast

I'm studying ARIMA at the moment with application to seasonal data sets. R lets you forecast using selected models but I'm just wondering what formula is used to compute these forecasts. For example, ...
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2k views

How can i do time series forecasting with missing data

I am relatively new to time series forecasting, I have worked previously with continuous data at regular intervals successfully, Now I have a data set with missing values, for example look at the ...
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769 views

Forecastability and Coefficient of Variation

I'm trying to get a sense check here. When determining "forecastability" for sales data, I tend to use the CV. However, this is highly susceptible to seasonality and outliers. As such, I was wondering:...
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1k views

Parameter estimation for dynamic regression models with correlated noise ARMA errors

I'm reading the Dynamic Regression Models chapter ( https://www.otexts.org/fpp/9/1 ) in Professor Hyndman's book, and I couldn't understand how to fit the regression model when the error is modeled ...
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0answers
2k views

Stock closing price forecasting using ARIMA model in R

I have downloaded the daily stock Adjusted Close price of one stock from sep 2011 to till date. As per my study plan, I have plotted some basic plots to understand the daily stock Adjusted closing ...
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10k views

Removing seasonality from data

I have a dataset depicting weekly revenue over time for a computer company. The plot for the data looks like this: I decomposed the data into its additive components using the ...
3
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0answers
153 views

Verification of assumptions in TBATS model

I have a question about using BATS/TBATS models implemented in the forecast package for R. In De Liv­era, Hyndman & Snyder (2011) the models are used without any following analysis. Is it OK to ...
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158 views

Sales forecasting to account for regression

I have a very beginner question. I am attempting to forecast total 2014 unit sales of a large number of products. The data I have has 10 points for each individual product, which are the total unit ...
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0answers
218 views

Why is the Chow test with $R^2$ wrong?

The regular way to compute the F-value for a Chow forecast test is: $$F=\frac{(e_R'e_R-e_1'e_1)/g}{e_1'e_1/(n-k)}$$ My professor said something today about that a Chow forecast test using $R^2$ would ...
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138 views

Should I de-mean a predictor variable before a dummy interaction

Suppose I have the following time-series linear model where $\beta$ is misspecified: $Y(t+1) = \alpha + \beta X(t) + \sum_{i=1}^{10000}\gamma_i Z_i(T) + \varepsilon$ where all parameters are in $\...
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0answers
242 views

How do I forecast a timeseries of data using GARCH(1,1)?

I'm new to GARCH, but I've got daily data of TV Ratings. I've been trying to forecast this for future, and a quick background - the data is non-stationary, has high seasonality (weekly, monthly & ...
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0answers
1k views

R forecast package: How to combine fourier terms with another XREG matrix

I'm using R forecast package with a daily time series data, that has complex i.e. Multiple seasonality (weekly, Yearly, monthly). The fit/forecast process also needs to take into account certain day ...
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131 views

Prediction model problem

I am trying to design a model that can estimate the number of customers I will receive in every store every month using the number of customers I received every month in every store for the last five ...
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0answers
161 views

Modeling relative contribution of a variable

I am overthinking this for sure, but I am stumped. I have a historical data set of projects with hours of contribution by various positions. There are six types of projects. How can I model the ...
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0answers
66 views

Updating a set of estimated forecasts

Suppose I have some stochastic process $X_t$. At each time $t$, I receive an estimated probability distribution for $x_t$, followed by an observation $x_t$. After receiving a set of observations ${x_1,...
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1answer
496 views

Fitting time series with outliers

I have daily sales data for a department store for the past 850 days. I have indicators on the major holidays and the days leading up to the major holidays. The number of days before the holidays that ...
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118 views

Estimating prediction interval of ARMA process using R forecast function

the theme is forecasting with ARMA models. I'm trying to understand how the R forecast function works if applied to an ...
2
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1answer
141 views

How to predict weekly or monthly sales from daily time series model?

I've been given daily data and I've trained a SARIMAX time series model in Python so that I can predict daily data if given daily input. However, I need to forecast on a monthly or weekly level, ...
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0answers
26 views

ARIMA predictors - clarification

I'm working on multivariate time series (still), and would like some clarification. I was reading this site: Duke Forecasting and I came across this statement: "We see that the most significant ...
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28 views

Forecasting using MA(2) model when past 5 observations are known

So given an MA(2) model : Xt = Wt + Theta1 * Wt-1 + Theta2 * Wt-2 Where Wt is white noise. (Normally distributed) and Theta1 and theta2 were available. Say if X96,X97,...X100 of the series were given ...