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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99 views

Should I avoid mixed ARMA models?

I have hourly demand data for taxi rides that spans several years into the past. I want to use it in order to forecast future demand (for the next day). Robert Nau warns against the usage of a mixed ...
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216 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
434 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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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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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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38 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 ...
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1answer
182 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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210 views

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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75 views

auto.arima picks seasonal model for non seasonal series

I have a monthly time series data of 3 years whose acf and pacf plots confirms absence of seasonality. But auto.arima picks a seasonal model by seasonal difference first and then seasonal AR component ...
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149 views

Obtaining from scratch the volatility in GARCH model using R?

I'm trying to obtain the same vector of volatility by myself $\sqrt{h_{t|t-1}}$ of a Garch Model, that I obtained "automatically" using the function "ugarchfit" from the package "rugarch". So after ...
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1answer
207 views

Flat forecast of trended time series data in r

I have a monthly time series of online visits for last 3 years starting from Jan 2016 to Dec 2018 and need to forecast for 2019. The data clearly has an upward trend although no seasonal lags ...
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80 views

Can the sum of several time-series be a white noise process, when the individual time series are not?

Intuitively, I think that it is possible for a sum of time series to be white noise, when the individual time series are not. Reason I am asking, is because I want to know if it's useful to ...
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195 views

Combinef function in R HTS pakage

First I would like to thank R people & package developpers for making available such a collection of great tools. My question is about forecasts reconciliation, as I find huge differences ...
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1answer
37 views

How can a person predicted best playing 11 in a match between two teams?

This website allows people to bet on cricket and football matches. They ask people to select 11 players and there are point system, so at the end whoever ends with more points gets lots of money. ...
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39 views

Time Series Prediction Model for Home Prices

I am building a time series model to predict the zillow home prices for march 2019.I have data for each zip code from the year 1993 - 2018 and i have prices for every month.I was trying to use ARIIMA ...
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46 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
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1answer
289 views

Adding noise to time series data to increase training data

I am dealing with a weekly time series forecasting problem and I am currently investigating the use of an LSTM to make a multi-step forecast for a univariate time series. I actually have a ...
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83 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 ...
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2answers
160 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 ...
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160 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 ...
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147 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 $\...
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65 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-...
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126 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 ...
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46 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 ...
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93 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 ...
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1answer
98 views

How can I split my time-series data into Train/Validation and Test set to apply Rolling Window

I am dealing with the LASSO regression in (pure AR-regressions) context. I have a lot of observations (around 4000). Therefore I would try to use the train/validation/test method. The idea was to ...
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47 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 ...
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44 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 ...
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35 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 ...
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1answer
254 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 ...
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178 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 ...
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1answer
32 views

Establishing the minimum required training set size, when cross validating time series data

I want to evaluate and compare how well various models perform with regards to modelling time series data (the data in question is daily revenue). It seems that cross validation error might be a ...
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235 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 ...
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38 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 ...
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196 views

Implementaiton of Continuous Ranked Probability Score (CRPS) when Observation is a Distribution

The most general form of the Continuous Ranked Probability Score (CRPS) is defined as, $\int_{\mathbb{R}} \big( \hat{F}^e(x) - F^0(x)\big)^2dx,$ for some true distribution, $F^0$, and empirical ...
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96 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
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1answer
54 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 ...
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77 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 ...
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159 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 ...
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148 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 ...
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405 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/...
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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: ...
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181 views

Tree model does't go well on trend

I am using sales time series data 2011 onwards, to make predictions for upto 2 years. Other than date and holiday related features, i created moving averages, y/y ratios and lags. I also extracted ...
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1answer
100 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 ...
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0answers
278 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 ...
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86 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 ...
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266 views

How much do the parameters in the Holt-Winters model matter?

When fitting a Holt-Winters model, I usually take the approach of retrospectively "predicting" some known historical values for the series, and optimising the coefficients for the parameters by ...
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1answer
350 views

A time series logit model with lagged dependent variable

I have a panel dataset for stocks. My goal is to model and predict if the stock will close positive (1) tomorrow based on today's close (1/0) and other macroeconomic and firm-specific variables.So I ...
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490 views

Alternatives to Holt-Winters models when the seasonality pattern has changed

I am forecasting a series of daily volumes in terms of units processed for a particular time period (the period around Christmas). Historically, I have used a Holt-Winters model, with the minor ...
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266 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 ...