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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30 views

Forecasting time series for categorical variables

I have time-series data with daily sales for shops and sold items. I would like to predict the number of each product sold in each store. What is the best way to solve this problem? It is necessary to ...
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Treating seasonality in Partial Least Squares forecast

I've been looking for answers on this question but couldn't find concrete solutions so wanted to ask y'all. I have been playing around trying to forecast an economic/financial-related indicator with ...
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2answers
107 views

Multi-step ahead forecasting with LSTM neural network

I would like to forecast the heat load of a district heating network given its past values, the temperature and the 3-day ahead forecast of the temperature with an LSTM RNN. The data is hourly and I ...
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29 views

How to Recursively Predict a Time Series Using Neural Networks

I am currently using neural networks to forecast an electrical demand time series. I am trying to create a forecast for the following day given previous observations at half hourly intervals. My ...
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221 views

Meta-Analysis on Effect Sizes with 95% Bayesian CI from CausalImpact R package

I am using the CausalImpact package in R to calculate the impact of a marketing intervention using Bayesian Structural Time Series. This methodology and package is explained in Broderson et al. 2015 ...
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1answer
37 views

Time series Data : Regress absolute values or regress the %growth of the values?

I am doing a time-series data analysis. The idea is to produce a forecast from the regression output. I am regressing Air traffic passengers of country A with GDP/capita of country A. I am getting ...
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time series forecasting - ljung-box test - degrees of freedom to subtract when working with breaks

I'm working on a differentiated seasonal time series with 2 breaks and non-zero mean. So, besides the constant i've got 2 dummies for breaks correction. Question: when performing LB test, if my model ...
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88 views

Big Mart Sales Prediction Problem

I hope that some of you are familiar with Big Mart sales prediction data that was provided by Analytics Vidhya as a contest. The problem statement of on the website is as follows: The data ...
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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 ...
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2answers
173 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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Vector Time Series: Capturing Systematic and Nonsystematic Patterns in Multiple Datasets | Financial Option Data

How does time series work with multiple time series data sets on the same index? For example, suppose I were a utilities company. Suppose I have the electricity usage of two homes, each indexed for ...
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933 views

Forecasting a ARIMA(1,1,1) model

ARIMA(1,1,1) process with constant term $\mu$ is $X_t=\alpha X_{t-1}+\mu+Z_t+\beta Z_{t-1}$ where $Z_t$ is white noise with mean zero variance $\sigma ^2$. Find one step and two step ahead forecast ...
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How to do regression on a time series by learning from historical time series?

I have a data set of customer purchases from the day of their registration to 120 days. There is a time series for each customer. However, some new customers do not have a history of 120 days yet. I ...
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Group time series examples

I am looking for group time series examples. I am working on two hierarchies and interested in interactions also. Couple of challenges I am facing I have 36 months data and many of the series has ...
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2k views

AIC versus cross validation in time series: the small sample case

I am interested in model selection in a time series setting. For concreteness, suppose I want to select an ARMA model from a pool of ARMA models with different lag orders. The ultimate intent is ...
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Forecasting in a state-space model from a Bayesian perspective

We have the following state-space model(or linear dynamical model): \begin{align} x_t&\sim N(Ax_{t-1},Q)\\ y_t&\sim N(Bx_{t},\Sigma) \end{align} I want to obtain a sample from $p(y_{T+1}\mid ...
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Big categorical data

I am trying to predict the price of used vehicles using three different models: Regression, ANN, and random forest. I am having 6 variables as an input for my model. One of my variables is the ...
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23 views

Forecasting when data limited to a particular period each year

I have sales data over a number of years for several different products. The products are only on sale for a limited period each year (spring - summer). The data will sometimes be irregularly spaced (...
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Is my Data stationary? KPSS, ADF Tests and ACF

I already differenced my Data by 1 and i am not sure whether my Data is now stationary or not. I perfomed an KPSS and ADF test in order to help me decide if it is. I think it is stationary but im not ...
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220 views

Forecasting with ARIMA models and sensor data in R

I'm trying to apply ARIMA models to sensor data and would be thankful if anyone could answer my questions. I should add that I have very little experience with time series (trying to change that). ...
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Accuracy of point forecasts vs. average accuracy of multistep forecasts?

It seems to me that it is possible that a forecasting model does very well on one step ahead forecasts (or on any other point forecast) but performs poorly on multistep forecasts (if you average the ...
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How to use forecast data in neural net when forecast produced periodically?

I am considering how to structure a neural net problem where an input forecast (say, for chocolate production) is produced for 'k' time periods. This forecast is produced every time period. and I ...
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110 views

Evaluating parameters of a time series model on multiple experimental sessions

I'm trying to evaluate a model for a time series, given many time series (plural). For example, i'm using the forecast package and in particular the ...
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246 views

Statistical demand forecasting

How is batch demand forecasting done in retail like in Walmart where number of products to forecast are very large in number and products are short lived i.e have less than 36 months of historical ...
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5k views

How does R calculate prediction intervals in the forecast package?

I have a large dataset with different factors that I want to forecast to the future. These forecasts I will then later on use as inputs for a Monte Carlo simulation. My idea would be to use arima ...
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1answer
96 views

How to model and generate forecasts for time series with missing observations? [duplicate]

I am trying to model some historical variables which are discontinuous. I am working with monthly observations so I have 12 observations per year. However, there are cases in which, for example, I ...
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1answer
85 views

Need advice concering forecasting next year based on irregular time-series - UPDATED

I need your advice with regards to the following inquiry: "Based on your observations, what could you say about the load for the same months in year 2019?" ...
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336 views

Is time-series an appropriate method to model data sampled at widely irregular time intervals ?

I am relatively inexperienced with data analysis. My question: Is time-series an appropriate method to fit trends to data sampled at widely-irregular time intervals such as forests of different ages ...
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Using the R forecast package with missing values and/or irregular time series

I am impressed by the R forecast package, as well as e.g. the zoo package for irregular time series and interpolation of missing ...
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761 views

Irregular Time Series

Please consider the following code (in R) ...
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483 views

Forecasting time series with missing data and irregular intervals

I have a data set of medical drug stock levels at health centres and I want to forecast monthly consumption over the following 3-6 months. However about 30%-40% of the data is missing and some of the ...
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Median-based Versus Average-based forecast? Which is better?

When generating forecasts (e.g., product-customer time series data), should we choose an average-based forecast or median-based forecast? I recently read a very nice article by Nicholas Vandeput on ...
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38 views

How are missing data handled in Time series estimation?

I am looking for most popular/theoretically sound methods for handling missing data in time series model (particularly ARMA class) estimation. Also what method is used in R (in arima and in forecast ...
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1answer
187 views

General forecasting equation for ARIMA(p,d,q)(P,D,Q)s

what is general forecasting equation for ARIMA(p,d,q)(P,D,Q)s.? I wrote this equation, can someone confirm if it is a correct one? If not, can someone correct it? Thank you in advance! $\overline{...
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ARMA Forecasting - Professional Work

I was curious how long does it take you to do ARMA forecasts in your professional environments? I'm getting started using the "Real Statistics" Add-On in Excel & I have only been familiar with ...
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302 views

Deep Learning based time series forecasting

According to the paper "Statistical and Machine Learning forecasting methods: Concerns and ways forward", it looks like the recent DNN-based approach has weaker predictive power in extrapolation, i.e. ...
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115 views

Predicting walking routes using PyTorch

I'm working on a project that uses sensors to monitor a persons location. These devices simply record the current GPS coordinates and ping them back to a server (the coordinates will then be converted ...
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102 views

Irregular seasonality defined as white noise?

I've got data of which I think it has a seasonality. My data has a peak in july/august and one in december. I have only data of 2014 and 2015, but in both of the cases this is happening. (See my graph)...
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5k views

Forecasting irregular time series (with R)

There are several methods to make forecasts of equidistant time series (e.g. Holt-Winters, ARIMA, ...). However I am currently working on the following irregular spaced data set, which has a varying ...
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581 views

Irregular Seasonality in time series

I understand seasonality of a time series normally means a cyclic component with constant frequency. For example, the frequency is 24 for daily cyclic trend of hourly data. One of the basic models ...
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1answer
19 views

Forecasting Process with Limited Historical Data and High Variance

I have a general inquiry regarding a project I am working on. I cannot reveal too much, but I would like to gauge the community here and hopefully be pointed towards the right direction in terms of ...
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1answer
131 views

Is it possible to model continuous time series with exogenous regressors?

I've got an irregularly spaced time series with regressors. I've found the R packages cts and ctsem for continuous time series, but they don't allow for exogenous variables. Is it possible to have ...
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1answer
222 views

Dealing with missing data in Time Series or non-constant time intervals for forecasting in R (ARIMA, Holt Winters, Theta)

I have a time series of sensor data from a machine. This machine is sometimes moved and thus there are big chunks of missing data, here is a plot of the data points: My goal is to try to start ...
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1answer
81 views

Forecasting recurring orders for an online subscription business using Facebook Prophet and R

I am analyzing data from a subscription model, in which a customer must pay a recurring price at a regular interval (30 days) for access to the product. EDIT -> Direct link to daily data: https://...
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43 views

How do you evaluate bias and/or quality of time-series forecasts

I am working on a financial model that will forecast the revenue a company generates over a fiscal quarter, and I am not sure of the best way to rigorously evaluate the bias in the model. Every day ...
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1answer
37 views

Modeling non-linear (short) time series and cross-validate them

beginner data scientist here. Time series analysis is a completly new area for me, so please correct me if i write something that makes no sense. I have many multivariante short time series, between ...
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1answer
171 views

Log-Transform/Pre-Processing Time Series before Similarity Matching

I have ~1500 time series data representing store sales (US$). All time series are of the same size with 52 weeks of data with no NA values. For a subset of 18 specific time series, I want to find the ...
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how to calculate safety stock from output of ARIMA model?

I have built an arima model using monthly sales as input suppose the output from ARIMA model is : How do we calculate safety stock for different lead times lead times (in days)?? ...
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1answer
37 views

multivariate time series: selecting a predictive model

I have a time series dataset that looks like this ...
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1answer
60 views

Chances a forecasting model exceeds/deceeds a specified threshold [closed]

I am interested in determining the confidence of a forecasting model with applications to quantitative finance. I have the following multivariate data $X$: \begin{align} X(t) \sim F_{X}(t) \end{...