Questions tagged [forecasting]

Prediction of the future events. It is a special case of [prediction], in the context of [time-series].

Filter by
Sorted by
Tagged with
0
votes
0answers
26 views

How to fit trained model on entire time series? Correct way to go about this

When forecasting future values of time series I have a hard time deciding which procedure to use regarding how to incorporate the data used in the test set for validation into the model and predict ...
0
votes
1answer
23 views

Time Series Case Study

In time series, to forecast for 6 months, how much past data is sufficient ? I am having 13 years of data in file in which the first 3-4 years data is going down and then data is going up for the ...
1
vote
1answer
48 views

Interpretation of p-values in ARIMA model

I am starting my journey in time series analysis and forecasting and I'm currently facing some doubts. If I fit an ARIMA model to my data and I get for example an ARIMA(2,1,2) in which the p-value ...
0
votes
1answer
29 views

If I have a time series forecast density that is bi-modal, does that mean that my data is heteroscedastic?

The title pretty much explains it already: If I have enough data points that I can plot my entire forecast density and it ends up looking like this, does it mean that it is heteroscedastic and I ...
1
vote
0answers
25 views

Is it correct to make a conclusion as to whether a model is best for weekly or daily forecasting by comparing the root mean square errors?

I am performing daily and weekly forecasts for 28 days and 4 weeks respectively. Once I have used the same model to obtain the respective forecasts an root mean square errors (RMSE), I will like to ...
0
votes
2answers
45 views

GARCH Model Estimation

I am analysing a GARCH(1,1) model under the assumption of t-Student distribution. In particular, I set the problem in the following way. I have a series ${y_t}, t \in{1,2,...,T}$ and I assume that: ...
0
votes
0answers
18 views

Autocorrelation and Partial autocorrelation plot interpretation

I'm dealing with ARIMA models and forecasts. I have a series like the one shown below: The mean and the standard deviation seem to be stationary. I calculated the autocorrelation and the partial ...
0
votes
1answer
26 views

Transforming Forecast back to its original trend and seasonality state

I have read multiple similar threads but unfortunately I am unable to extract an answer for my question from it. I am making my own code for ARIMA on python without using python packages for ARIMA. I ...
0
votes
1answer
10 views

Recency weightage on stock forecast error

Say I want to forecast retail stock for 1 month, on daily basis. The error will be calculated using SMAPE, but I would weight the error using recency, i.e., the nearer the weight from now the higher ...
2
votes
1answer
35 views

Build ARIMA model equation with exogenous variable or regressors

I have a ARIMA model with three regressors as follow ...
6
votes
1answer
91 views

How do I decide when to use MAPE, SMAPE and MASE for time series analysis on stock forecasting

My task is to forecast future 1 month stock required for retail store, at a daily basis. How do I decide whether MAPE, SMAPE and MASE is a good metrics for the scenario? In my context, over-forecast ...
0
votes
0answers
6 views

ANN vs HEDONIC house price predictions [closed]

How to combine an artificial neural network model with a regression Hedonic model for house price ?
-1
votes
1answer
18 views

Fitted values of an Arimax model changing the X var [closed]

I estimate an arimax model $y_t=a*y_{t-1} + b*x_t + e_t$ Where $x_t$ is a dummy with ones from a specific date and on. $(0,0,0,...,0,1,1,1,1,1)$ I estimate it with Arima instruction forecast ...
0
votes
0answers
7 views

why is the level equation in the holt winters triple exponential model different from the other two?

the double exponential model is so simple: level: $s_t = \alpha x_t + (1-\alpha)(s_{t-1}+b_{t-1})$ trend: $b_t = \beta (s_t - s_{t-1}) + (1-\beta)b_{t-1}$ both intuitively weigh the new information ...
1
vote
1answer
94 views

Build SARIMA model equation with exogenous variable or regressors

I have a SARIMA model with one regressor (X): ...
0
votes
1answer
28 views

GARCH model prediction

I was analyzing a GARCH(1,1) process. In particular, let's say that I have a process ${y_t}$, with $t \in {1,2,...,T}$. I have created a GARCH process that can be written as: $\sigma_t^2 = \omega + \...
0
votes
1answer
45 views

Time series quantile regression

I have time series where at each time step I have a bunch of real-valued points (e.g. individual purchases on a given day), and would like to produce a forecast of several quantiles. One approach I'm ...
0
votes
1answer
26 views

ARIMA(0,1,1) repeating the same point estimate

I have a weekly series stored in a tsibble called data: ...
0
votes
0answers
10 views

Unplausible results in Hansens SPA-Test due to max(0, x)

In applying Hansens Superior Predictive Ability Test, I am receiving inplausible results for very bad alternatives, i.e. the p-value is 0 but should be in higher regions where you usually do not ...
0
votes
0answers
13 views

Interpret output auto.arima (1,0,0) (0,1,0) [12] with drift

Hi Auto.arina returned the followingoutput. I am trying to understand what is important to report here? What does the values of the coefficient tell me? what about sigma2? Log likelihood and AIC AIC ...
1
vote
1answer
43 views

ARIMA Forecasting Accuracy

I am working on the revision to a manuscript in which I used Google Trends data to evaluate whether a particular event affected Google search patterns. I used an ARIMA model to forecast the expected ...
1
vote
0answers
20 views

Missing as opposed to non existent data in time-series forecasting

Suppose you have a set of observations that occur at regular intervals in time, but containing regular gaps during which there is no data, not because it is hidden or missing, but because the ...
0
votes
1answer
33 views

How to forecast with time series of different length?

I am new to time series analysis, and I am wondering how I can approach forecasting having time series of different lengths. Specifically, each time series contains a sequence of ages and value. E.g., ...
1
vote
0answers
18 views

Incorporate recent drop in number of units sold in a forecast using exponential smoothing

I'm trying to generate a one-year forecast for the number of units sold by a retail company. I'm using monthly data from 2017 and 2018. The forecast is for 2019, and I'm using the data from the months ...
2
votes
1answer
29 views

Multicollinearity when modeling regression with ARIMA errors

When we fit regression with ARIMA errors, how do we access multicollinearity problem? If it is an lm model, I can use variance inflation factor (VIF) and if the VIF value is greater than 10 (or 5), I ...
1
vote
0answers
25 views

How to train an LSTM model on multiple single-variable time-series data?

I am quite new to the field. I am working on a problem involving time-series forecasting of single variable time-series. Data is collected from the pressure sensor on a patient in hospital. Time ...
1
vote
1answer
48 views

Time series Count-data forecasting in R [closed]

I have sales data shows the number of units sold (so it is a count data). I am evaluating the following models: the lightgbm Count Time Series Following Generalised Linear Models (R tscount library) ...
1
vote
0answers
7 views

Does forecast error variance decomposition in which the response variable predominately explains itself imply the model is incorrectly specified?

So I have set up a six variable VAR model in the hope of explain natural gas prices and performed forecast error variance decomposition, however the response variable (natural gas prices) explains ...
0
votes
1answer
17 views

How to Predict the sales of all the items, offered in all the countries

I am working on a task to predict the sales of all the items offered in all the countries. The sales are aggregated on a daily and country level. Each Item has a history of past sales and prices for a ...
0
votes
0answers
24 views

Replacing Missing data by mean for important feature in ANN

I am working with failure prediction application of an artificial neural network. My data set consist of 29 days (data utilization trace over a 29-day period, CPU usage, machine failure, ..etc). ...
0
votes
1answer
49 views

ARIMA + Rolling Window

I'm currently working on building an ARIMA+GARCH model using R. My dataset consists of the logarithmic returns of the Dow Jones index for a period of 11 years 2005-2016, however, it's worth noting ...
1
vote
1answer
40 views

Cyclicality in time series

The high amount of cyclicality in the lynx time series makes it very difficult to model with ets and ...
0
votes
0answers
18 views

Forecasting a binary time series [duplicate]

My (real) problem is as follows: I have a weekly time series about orders of a given product in a specific bar. Let's say that we have a 0 when the bar doesn't order in that week and 1 when it does. ...
0
votes
0answers
16 views

Error terms (Et) of (S)ARIMA equation outisde R program

(EDITED) I used R forecast package and the best fit resulted in (1,1,2)(1,0,1) [7] with one regressor variable (X). I built the equation as follow: ...
0
votes
0answers
16 views

Is applying an ARMA model to a stationary series the same as applying it to a trend and seasonally adjusted series?

Is it true that regular differencing and seasonal differencing of a time-series to achieve stationarity, is the same thing as adjusting a time-series for trend and seasonality? If the above statement ...
0
votes
0answers
22 views

KPSS test thinks that regression is spurious

It should be obvious that there is a relationship between the market price of black pepper and the market price of white pepper. ...
0
votes
0answers
14 views

Detecting spurious regression by testing the residuals

A linear regression between "Number of Australian Air Passengers" and "Rice Production in Guinea" reveals a "strong" but probably spurious relationship between the two time series. ...
0
votes
0answers
13 views

Is Box-Jenkins approach to time-series prediction and forecasting similar to Unobserved Components models approach?

How I understand the Box-Jenkins Method in a nut-shell is that a time-series model has signals that can be identified by weighting its own past lagged values, or weighting its owned past errors or ...
0
votes
1answer
59 views

Incorporate additional information in Stock Forecasting

I am trying to forecast stock of health products. Other than historical stock quantity, I would have some other information, e.g.,: Certain stocks are in compete of each other; Certain stocks are ...
0
votes
1answer
38 views

Is time series analysis suitable for long term predicting/forecasting?

Can I use time series analysis to predict/forecast long term ? Example using ARIMA, how can I explain the back of the theory its?
2
votes
1answer
129 views

Estimate the time series like an event was never happened

I have data from a website where a specific advertising campaign happened a couple of years ago. What I want to do is to estimate how the signups on that website would have been without that big ...
0
votes
0answers
25 views

ARIMA(0, 1, 0) or ARIMA(0, 0, 0) for Stock log-Returns Forecast

I'm trying to forecast the log-returns of Amazon's stocks using the ARIMA model, so I went through the traditional procedure of examining the autocorrelation plot and the partial autocorrelation plot ...
0
votes
0answers
46 views

If prediction intervals become narrower when less historical data is provided, how do you justify using a full range of data?

In forecasting (ets) annual data, I notice that when I use the full data set of 10 years, the prediction intervals are much wider than when using an abridged version of the data set (5 years). I ...
0
votes
0answers
9 views

How to find FFT for a window period of a given large time series data?

If i binned time series data for particular time interval 't' and choose a window period of lets say 5 bins and converted into n rows as train data(5 bins for each row) and a y_value(need to be ...
0
votes
0answers
32 views

Diebold Mariano test Nested Models

I have computed forecasts with 4 different methods, namely OLS, Elastic Net, Cubic splines in combination with Lasso, and Neural Network. All models use the same set of base variables, except cubic ...
1
vote
2answers
38 views

How do I forecast quarterly public expenses based on annual budgets and potentially other variables?

I have some time series data from 2008 and forward (see below) on quarterly public expenses and annual public budgets. I would like to forecast the last two quarters of 2018 as precisely as possible, ...
0
votes
0answers
5 views

Significance of hyper parameters in the DHR model in R forecast package

The Dynamic Harmonic Regression model in R requires the input of parameters K, the length of which depends on the number of seasonality in the forecast data. According to https://otexts.com/fpp2/dhr....
1
vote
0answers
112 views

Neural Network regression on time series

I want to predict the trend values of a time serie [Y] based on the effect of other 10 input variables which can also have interaction. Since the combination of interaction between the inputs is ...
0
votes
1answer
26 views

ARIMA Time Series Simulation - Media Mix Model

I have designed and tested a time series model where I am able to examine the impact of various marketing channels on dependent variables (Such as sales, revenue, website traffic, etc). The model has ...
0
votes
0answers
10 views

Is it possible to to compare ARIMA and ARMA-GARCH model with different series?

I have some questions with model compares ion and forecasting. The row data (quarterly traffic accident numbers) is not stationary but it is stationary at first difference. we can model and forecast ...