Dynamic linear fashions with tfprobability

Welcome to the world of state area fashions. On this world, there’s a latent course of, hidden from our eyes; and there are observations we make in regards to the issues it produces. The method evolves resulting from some hidden logic (transition mannequin); and the way in which it produces the observations follows some hidden logic (commentary mannequin). There may be noise in course of evolution, and there may be noise in commentary. If the transition and commentary fashions each are linear, and the method in addition to commentary noise are Gaussian, we’ve a linear-Gaussian state area mannequin (SSM). The duty is to deduce the latent state from the observations. Essentially the most well-known approach is the Kálmán filter.

In sensible functions, two traits of linear-Gaussian SSMs are particularly engaging.

For one, they allow us to estimate dynamically altering parameters. In regression, the parameters will be seen as a hidden state; we might thus have a slope and an intercept that change over time. When parameters can differ, we communicate of dynamic linear fashions (DLMs). That is the time period we’ll use all through this publish when referring to this class of fashions.

Second, linear-Gaussian SSMs are helpful in time-series forecasting as a result of Gaussian processes will be added. A time sequence can thus be framed as, e.g. the sum of a linear development and a course of that varies seasonally.

Utilizing tfprobability, the R wrapper to TensorFlow Chance, we illustrate each elements right here. Our first instance will probably be on dynamic linear regression. In an in depth walkthrough, we present on easy methods to match such a mannequin, easy methods to acquire filtered, in addition to smoothed, estimates of the coefficients, and easy methods to acquire forecasts.
Our second instance then illustrates course of additivity. This instance will construct on the primary, and may function a fast recap of the general process.

Let’s bounce in.

Dynamic linear regression instance: Capital Asset Pricing Mannequin (CAPM)

Our code builds on the just lately launched variations of TensorFlow and TensorFlow Chance: 1.14 and 0.7, respectively.

Notice how there’s one factor we used to do these days that we’re not doing right here: We’re not enabling keen execution. We are saying why in a minute.

Our instance is taken from Petris et al.(2009)(Petris, Petrone, and Campagnoli 2009), chapter 3.2.7.
Moreover introducing the dlm package deal, this e-book supplies a pleasant introduction to the concepts behind DLMs basically.

For instance dynamic linear regression, the authors characteristic a dataset, initially from Berndt(1991)(Berndt 1991) that has month-to-month returns, collected from January 1978 to December 1987, for 4 totally different shares, the 30-day Treasury Invoice – standing in for a risk-free asset –, and the value-weighted common returns for all shares listed on the New York and American Inventory Exchanges, representing the general market returns.

Let’s have a look.

# As the info doesn't appear to be accessible on the deal with given in Petris et al. any extra,
# we put it on the weblog for obtain
# obtain from: 
# https://github.com/rstudio/tensorflow-blog/blob/grasp/docs/posts/2019-06-25-dynamic_linear_models_tfprobability/knowledge/capm.txt"
df <- read_table(
  "capm.txt",
  col_types = checklist(X1 = col_date(format = "%Y.%m"))) %>%
  rename(month = X1)
df %>% glimpse()

Observations: 120
Variables: 7
$ month  <date> 1978-01-01, 1978-02-01, 1978-03-01, 1978-04-01, 1978-05-01, 19…
$ MOBIL  <dbl> -0.046, -0.017, 0.049, 0.077, -0.011, -0.043, 0.028, 0.056, 0.0…
$ IBM    <dbl> -0.029, -0.043, -0.063, 0.130, -0.018, -0.004, 0.092, 0.049, -0…
$ WEYER  <dbl> -0.116, -0.135, 0.084, 0.144, -0.031, 0.005, 0.164, 0.039, -0.0…
$ CITCRP <dbl> -0.115, -0.019, 0.059, 0.127, 0.005, 0.007, 0.032, 0.088, 0.011…
$ MARKET <dbl> -0.045, 0.010, 0.050, 0.063, 0.067, 0.007, 0.071, 0.079, 0.002,…
$ RKFREE <dbl> 0.00487, 0.00494, 0.00526, 0.00491, 0.00513, 0.00527, 0.00528, …

df %>% collect(key = "image", worth = "return", -month) %>%
  ggplot(aes(x = month, y = return, coloration = image)) +
  geom_line() +
  facet_grid(rows = vars(image), scales = "free")

Monthly returns for selected stocks; data from Berndt (1991).

Determine 1: Month-to-month returns for chosen shares; knowledge from Berndt (1991).

The Capital Asset Pricing Mannequin then assumes a linear relationship between the surplus returns of an asset beneath research and the surplus returns of the market. For each, extra returns are obtained by subtracting the returns of the chosen risk-free asset; then, the scaling coefficient between them reveals the asset to both be an “aggressive” funding (slope > 1: adjustments available in the market are amplified), or a conservative one (slope < 1: adjustments are damped).

Assuming this relationship doesn’t change over time, we will simply use lm for example this. Following Petris et al. in zooming in on IBM because the asset beneath research, we’ve

# extra returns of the asset beneath research
ibm <- df$IBM - df$RKFREE
# market extra returns
x <- df$MARKET - df$RKFREE

match <- lm(ibm ~ x)
abstract(match)

Name:
lm(method = ibm ~ x)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.11850 -0.03327 -0.00263  0.03332  0.15042 

Coefficients:
              Estimate Std. Error t worth Pr(>|t|)    
(Intercept) -0.0004896  0.0046400  -0.106    0.916    
x            0.4568208  0.0675477   6.763 5.49e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual customary error: 0.05055 on 118 levels of freedom
A number of R-squared:  0.2793,    Adjusted R-squared:  0.2732 
F-statistic: 45.74 on 1 and 118 DF,  p-value: 5.489e-10

So IBM is discovered to be a conservative funding, the slope being ~ 0.5. However is that this relationship secure over time?

Let’s flip to tfprobability to analyze.

We need to use this instance to show two important functions of DLMs: acquiring smoothing and/or filtering estimates of the coefficients, in addition to forecasting future values. So in contrast to Petris et al., we divide the dataset right into a coaching and a testing half:.

# zoom in on ibm
ts <- ibm %>% matrix()
# forecast 12 months
n_forecast_steps <- 12
ts_train <- ts[1:(length(ts) - n_forecast_steps), 1, drop = FALSE]

# ensure that we work with float32 right here
ts_train <- tf$forged(ts_train, tf$float32)
ts <- tf$forged(ts, tf$float32)

We now assemble the mannequin. sts_dynamic_linear_regression() does what we would like:

# outline the mannequin on the entire sequence
linreg <- ts %>%
  sts_dynamic_linear_regression(
    design_matrix = cbind(rep(1, size(x)), x) %>% tf$forged(tf$float32)
  )

We cross it the column of extra market returns, plus a column of ones, following Petris et al.. Alternatively, we might middle the only predictor – this may work simply as nicely.

How are we going to coach this mannequin? Methodology-wise, we’ve a selection between variational inference (VI) and Hamiltonian Monte Carlo (HMC). We’ll see each. The second query is: Are we going to make use of graph mode or keen mode? As of at this time, for each VI and HMC, it’s most secure – and quickest – to run in graph mode, so that is the one approach we present. In a number of weeks, or months, we must always have the ability to prune a number of sess$run()s from the code!

Usually in posts, when presenting code we optimize for simple experimentation (that means: line-by-line executability), not modularity. This time although, with an vital variety of analysis statements concerned, it’s best to pack not simply the becoming, however the smoothing and forecasting as nicely right into a perform (which you might nonetheless step by if you happen to needed). For VI, we’ll have a match _with_vi perform that does all of it. So after we now begin explaining what it does, don’t kind within the code simply but – it’ll all reappear properly packed into that perform, so that you can copy and execute as an entire.

Becoming a time sequence with variational inference

Becoming with VI just about seems like coaching historically used to look in graph-mode TensorFlow. You outline a loss – right here it’s accomplished utilizing sts_build_factored_variational_loss() –, an optimizer, and an operation for the optimizer to cut back that loss:

optimizer <- tf$compat$v1$prepare$AdamOptimizer(0.1)

# solely prepare on the coaching set!    
loss_and_dists <- ts_train %>% sts_build_factored_variational_loss(mannequin = mannequin)
variational_loss <- loss_and_dists[[1]]

train_op <- optimizer$reduce(variational_loss)

Notice how the loss is outlined on the coaching set solely, not the entire sequence.

Now to truly prepare the mannequin, we create a session and run that operation:

 with (tf$Session() %as% sess,  {
      
      sess$run(tf$compat$v1$global_variables_initializer())
   
      for (step in 1:n_iterations) {
        res <- sess$run(train_op)
        loss <- sess$run(variational_loss)
        if (step %% 10 == 0)
          cat("Loss: ", as.numeric(loss), "n")
      }
 })

Given we’ve that session, let’s make use of it and compute all of the estimates we want.
Once more, – the next snippets will find yourself within the fit_with_vi perform, so don’t run them in isolation simply but.

Acquiring forecasts

The very first thing we would like for the mannequin to present us are forecasts. With the intention to create them, it wants samples from the posterior. Fortunately we have already got the posterior distributions, returned from sts_build_factored_variational_loss, so let’s pattern from them and cross them to sts_forecast:

variational_distributions <- loss_and_dists[[2]]
posterior_samples <-
  Map(
    perform(d) d %>% tfd_sample(n_param_samples),
    variational_distributions %>% reticulate::py_to_r() %>% unname()
  )
forecast_dists <- ts_train %>% sts_forecast(mannequin, posterior_samples, n_forecast_steps)

sts_forecast() returns distributions, so we name tfd_mean() to get the posterior predictions and tfd_stddev() for the corresponding customary deviations:

fc_means <- forecast_dists %>% tfd_mean()
fc_sds <- forecast_dists %>% tfd_stddev()

By the way in which – as we’ve the complete posterior distributions, we’re not at all restricted to abstract statistics! We might simply use tfd_sample() to acquire particular person forecasts.

Smoothing and filtering (Kálmán filter)

Now, the second (and final, for this instance) factor we’ll need are the smoothed and filtered regression coefficients. The well-known Kálmán Filter is a Bayesian-in-spirit methodology the place at every time step, predictions are corrected by how a lot they differ from an incoming commentary. Filtering estimates are primarily based on observations we’ve seen up to now; smoothing estimates are computed “in hindsight,” making use of the entire time sequence.

We first create a state area mannequin from our time sequence definition:

# solely do that on the coaching set
# returns an occasion of tfd_linear_gaussian_state_space_model()
ssm <- mannequin$make_state_space_model(size(ts_train), param_vals = posterior_samples)

tfd_linear_gaussian_state_space_model(), technically a distribution, supplies the Kálmán filter functionalities of smoothing and filtering.

To acquire the smoothed estimates:

c(smoothed_means, smoothed_covs) %<-% ssm$posterior_marginals(ts_train)

And the filtered ones:

c(., filtered_means, filtered_covs, ., ., ., .) %<-% ssm$forward_filter(ts_train)

Lastly, we have to consider all these.

c(posterior_samples, fc_means, fc_sds, smoothed_means, smoothed_covs, filtered_means, filtered_covs) %<-%
  sess$run(checklist(posterior_samples, fc_means, fc_sds, smoothed_means, smoothed_covs, filtered_means, filtered_covs))

Placing all of it collectively (the VI version)

So right here’s the entire perform, fit_with_vi, prepared for us to name.

fit_with_vi <-
  perform(ts,
           ts_train,
           mannequin,
           n_iterations,
           n_param_samples,
           n_forecast_steps,
           n_forecast_samples) {
    
    optimizer <- tf$compat$v1$prepare$AdamOptimizer(0.1)
    
    loss_and_dists <-
      ts_train %>% sts_build_factored_variational_loss(mannequin = mannequin)
    variational_loss <- loss_and_dists[[1]]
    train_op <- optimizer$reduce(variational_loss)
    
    with (tf$Session() %as% sess,  {
      
      sess$run(tf$compat$v1$global_variables_initializer())
      for (step in 1:n_iterations) {
        sess$run(train_op)
        loss <- sess$run(variational_loss)
        if (step %% 1 == 0)
          cat("Loss: ", as.numeric(loss), "n")
      }
      variational_distributions <- loss_and_dists[[2]]
      posterior_samples <-
        Map(
          perform(d)
            d %>% tfd_sample(n_param_samples),
          variational_distributions %>% reticulate::py_to_r() %>% unname()
        )
      forecast_dists <-
        ts_train %>% sts_forecast(mannequin, posterior_samples, n_forecast_steps)
      fc_means <- forecast_dists %>% tfd_mean()
      fc_sds <- forecast_dists %>% tfd_stddev()
      
      ssm <- mannequin$make_state_space_model(size(ts_train), param_vals = posterior_samples)
      c(smoothed_means, smoothed_covs) %<-% ssm$posterior_marginals(ts_train)
      c(., filtered_means, filtered_covs, ., ., ., .) %<-% ssm$forward_filter(ts_train)
   
      c(posterior_samples, fc_means, fc_sds, smoothed_means, smoothed_covs, filtered_means, filtered_covs) %<-%
        sess$run(checklist(posterior_samples, fc_means, fc_sds, smoothed_means, smoothed_covs, filtered_means, filtered_covs))
      
    })
    
    checklist(
      variational_distributions,
      posterior_samples,
      fc_means[, 1],
      fc_sds[, 1],
      smoothed_means,
      smoothed_covs,
      filtered_means,
      filtered_covs
    )
  }

And that is how we name it.

# variety of VI steps
n_iterations <- 300
# pattern dimension for posterior samples
n_param_samples <- 50
# pattern dimension to attract from the forecast distribution
n_forecast_samples <- 50

# here is the mannequin once more
mannequin <- ts %>%
  sts_dynamic_linear_regression(design_matrix = cbind(rep(1, size(x)), x) %>% tf$forged(tf$float32))

# name fit_vi outlined above
c(
  param_distributions,
  param_samples,
  fc_means,
  fc_sds,
  smoothed_means,
  smoothed_covs,
  filtered_means,
  filtered_covs
) %<-% fit_vi(
  ts,
  ts_train,
  mannequin,
  n_iterations,
  n_param_samples,
  n_forecast_steps,
  n_forecast_samples
)

Curious in regards to the outcomes? We’ll see them in a second, however earlier than let’s simply shortly look on the different coaching methodology: HMC.

Placing all of it collectively (the HMC version)

tfprobability supplies sts_fit_with_hmc to suit a DLM utilizing Hamiltonian Monte Carlo. Latest posts (e.g., Hierarchical partial pooling, continued: Various slopes fashions with TensorFlow Chance) confirmed easy methods to arrange HMC to suit hierarchical fashions; right here a single perform does all of it.

Right here is fit_with_hmc, wrapping sts_fit_with_hmc in addition to the (unchanged) strategies for acquiring forecasts and smoothed/filtered parameters:

num_results <- 200
num_warmup_steps <- 100

fit_hmc <- perform(ts,
                    ts_train,
                    mannequin,
                    num_results,
                    num_warmup_steps,
                    n_forecast,
                    n_forecast_samples) {
  
  states_and_results <-
    ts_train %>% sts_fit_with_hmc(
      mannequin,
      num_results = num_results,
      num_warmup_steps = num_warmup_steps,
      num_variational_steps = num_results + num_warmup_steps
    )
  
  posterior_samples <- states_and_results[[1]]
  forecast_dists <-
    ts_train %>% sts_forecast(mannequin, posterior_samples, n_forecast_steps)
  fc_means <- forecast_dists %>% tfd_mean()
  fc_sds <- forecast_dists %>% tfd_stddev()
  
  ssm <-
    mannequin$make_state_space_model(size(ts_train), param_vals = posterior_samples)
  c(smoothed_means, smoothed_covs) %<-% ssm$posterior_marginals(ts_train)
  c(., filtered_means, filtered_covs, ., ., ., .) %<-% ssm$forward_filter(ts_train)
  
  with (tf$Session() %as% sess,  {
    sess$run(tf$compat$v1$global_variables_initializer())
    c(
      posterior_samples,
      fc_means,
      fc_sds,
      smoothed_means,
      smoothed_covs,
      filtered_means,
      filtered_covs
    ) %<-%
      sess$run(
        checklist(
          posterior_samples,
          fc_means,
          fc_sds,
          smoothed_means,
          smoothed_covs,
          filtered_means,
          filtered_covs
        )
      )
  })
  
  checklist(
    posterior_samples,
    fc_means[, 1],
    fc_sds[, 1],
    smoothed_means,
    smoothed_covs,
    filtered_means,
    filtered_covs
  )
}

c(
  param_samples,
  fc_means,
  fc_sds,
  smoothed_means,
  smoothed_covs,
  filtered_means,
  filtered_covs
) %<-% fit_hmc(ts,
               ts_train,
               mannequin,
               num_results,
               num_warmup_steps,
               n_forecast,
               n_forecast_samples)

Now lastly, let’s check out the forecasts and filtering resp. smoothing estimates.

Forecasts

Placing all we’d like into one dataframe, we’ve

smoothed_means_intercept <- smoothed_means[, , 1] %>% colMeans()
smoothed_means_slope <- smoothed_means[, , 2] %>% colMeans()

smoothed_sds_intercept <- smoothed_covs[, , 1, 1] %>% colMeans() %>% sqrt()
smoothed_sds_slope <- smoothed_covs[, , 2, 2] %>% colMeans() %>% sqrt()

filtered_means_intercept <- filtered_means[, , 1] %>% colMeans()
filtered_means_slope <- filtered_means[, , 2] %>% colMeans()

filtered_sds_intercept <- filtered_covs[, , 1, 1] %>% colMeans() %>% sqrt()
filtered_sds_slope <- filtered_covs[, , 2, 2] %>% colMeans() %>% sqrt()

forecast_df <- df %>%
  choose(month, IBM) %>%
  add_column(pred_mean = c(rep(NA, size(ts_train)), fc_means)) %>%
  add_column(pred_sd = c(rep(NA, size(ts_train)), fc_sds)) %>%
  add_column(smoothed_means_intercept = c(smoothed_means_intercept, rep(NA, n_forecast_steps))) %>%
  add_column(smoothed_means_slope = c(smoothed_means_slope, rep(NA, n_forecast_steps))) %>%
  add_column(smoothed_sds_intercept = c(smoothed_sds_intercept, rep(NA, n_forecast_steps))) %>%
  add_column(smoothed_sds_slope = c(smoothed_sds_slope, rep(NA, n_forecast_steps))) %>%
  add_column(filtered_means_intercept = c(filtered_means_intercept, rep(NA, n_forecast_steps))) %>%
  add_column(filtered_means_slope = c(filtered_means_slope, rep(NA, n_forecast_steps))) %>%
  add_column(filtered_sds_intercept = c(filtered_sds_intercept, rep(NA, n_forecast_steps))) %>%
  add_column(filtered_sds_slope = c(filtered_sds_slope, rep(NA, n_forecast_steps)))

So right here first are the forecasts. We’re utilizing the estimates returned from VI, however we might simply as nicely have used these from HMC – they’re practically indistinguishable. The identical goes for the filtering and smoothing estimates displayed beneath.

ggplot(forecast_df, aes(x = month, y = IBM)) +
  geom_line(coloration = "gray") +
  geom_line(aes(y = pred_mean), coloration = "cyan") +
  geom_ribbon(
    aes(ymin = pred_mean - 2 * pred_sd, ymax = pred_mean + 2 * pred_sd),
    alpha = 0.2,
    fill = "cyan"
  ) +
  theme(axis.title = element_blank())

12-point-ahead forecasts for IBM; posterior means +/- 2 standard deviations.

Determine 2: 12-point-ahead forecasts for IBM; posterior means +/- 2 customary deviations.

Smoothing estimates

Listed below are the smoothing estimates. The intercept (proven in orange) stays fairly secure over time, however we do see a development within the slope (displayed in inexperienced).

ggplot(forecast_df, aes(x = month, y = smoothed_means_intercept)) +
  geom_line(coloration = "orange") +
  geom_line(aes(y = smoothed_means_slope),
            coloration = "inexperienced") +
  geom_ribbon(
    aes(
      ymin = smoothed_means_intercept - 2 * smoothed_sds_intercept,
      ymax = smoothed_means_intercept + 2 * smoothed_sds_intercept
    ),
    alpha = 0.3,
    fill = "orange"
  ) +
  geom_ribbon(
    aes(
      ymin = smoothed_means_slope - 2 * smoothed_sds_slope,
      ymax = smoothed_means_slope + 2 * smoothed_sds_slope
    ),
    alpha = 0.1,
    fill = "inexperienced"
  ) +
  coord_cartesian(xlim = c(forecast_df$month[1], forecast_df$month[length(ts) - n_forecast_steps]))  +
  theme(axis.title = element_blank())

Smoothing estimates from the Kálmán filter. Green: coefficient for dependence on excess market returns (slope), orange: vector of ones (intercept).

Determine 3: Smoothing estimates from the Kálmán filter. Inexperienced: coefficient for dependence on extra market returns (slope), orange: vector of ones (intercept).

Filtering estimates

For comparability, listed here are the filtering estimates. Notice that the y-axis extends additional up and down, so we will seize uncertainty higher:

ggplot(forecast_df, aes(x = month, y = filtered_means_intercept)) +
  geom_line(coloration = "orange") +
  geom_line(aes(y = filtered_means_slope),
            coloration = "inexperienced") +
  geom_ribbon(
    aes(
      ymin = filtered_means_intercept - 2 * filtered_sds_intercept,
      ymax = filtered_means_intercept + 2 * filtered_sds_intercept
    ),
    alpha = 0.3,
    fill = "orange"
  ) +
  geom_ribbon(
    aes(
      ymin = filtered_means_slope - 2 * filtered_sds_slope,
      ymax = filtered_means_slope + 2 * filtered_sds_slope
    ),
    alpha = 0.1,
    fill = "inexperienced"
  ) +
  coord_cartesian(ylim = c(-2, 2),
                  xlim = c(forecast_df$month[1], forecast_df$month[length(ts) - n_forecast_steps])) +
  theme(axis.title = element_blank())

Filtering estimates from the Kálmán filter. Green: coefficient for dependence on excess market returns (slope), orange: vector of ones (intercept).

Determine 4: Filtering estimates from the Kálmán filter. Inexperienced: coefficient for dependence on extra market returns (slope), orange: vector of ones (intercept).

Up to now, we’ve seen a full instance of time-series becoming, forecasting, and smoothing/filtering, in an thrilling setting one doesn’t encounter too usually: dynamic linear regression. What we haven’t seen as but is the additivity characteristic of DLMs, and the way it permits us to decompose a time sequence into its (theorized) constituents.
Let’s do that subsequent, in our second instance, anti-climactically making use of the iris of time sequence, AirPassengers. Any guesses what parts the mannequin would possibly presuppose?

Determine 5: AirPassengers.

Composition instance: AirPassengers

Libraries loaded, we put together the info for tfprobability:

The mannequin is a sum – cf. sts_sum – of a linear development and a seasonal part:

linear_trend <- ts %>% sts_local_linear_trend()
month-to-month <- ts %>% sts_seasonal(num_seasons = 12)
mannequin <- ts %>% sts_sum(parts = checklist(month-to-month, linear_trend))

Once more, we might use VI in addition to MCMC to coach the mannequin. Right here’s the VI method:

n_iterations <- 100
n_param_samples <- 50
n_forecast_samples <- 50

optimizer <- tf$compat$v1$prepare$AdamOptimizer(0.1)

fit_vi <-
  perform(ts,
           ts_train,
           mannequin,
           n_iterations,
           n_param_samples,
           n_forecast_steps,
           n_forecast_samples) {
    loss_and_dists <-
      ts_train %>% sts_build_factored_variational_loss(mannequin = mannequin)
    variational_loss <- loss_and_dists[[1]]
    train_op <- optimizer$reduce(variational_loss)
    
    with (tf$Session() %as% sess,  {
      sess$run(tf$compat$v1$global_variables_initializer())
      for (step in 1:n_iterations) {
        res <- sess$run(train_op)
        loss <- sess$run(variational_loss)
        if (step %% 1 == 0)
          cat("Loss: ", as.numeric(loss), "n")
      }
      variational_distributions <- loss_and_dists[[2]]
      posterior_samples <-
        Map(
          perform(d)
            d %>% tfd_sample(n_param_samples),
          variational_distributions %>% reticulate::py_to_r() %>% unname()
        )
      forecast_dists <-
        ts_train %>% sts_forecast(mannequin, posterior_samples, n_forecast_steps)
      fc_means <- forecast_dists %>% tfd_mean()
      fc_sds <- forecast_dists %>% tfd_stddev()
      
      c(posterior_samples,
        fc_means,
        fc_sds) %<-%
        sess$run(checklist(posterior_samples,
                      fc_means,
                      fc_sds))
    })
    
    checklist(variational_distributions,
         posterior_samples,
         fc_means[, 1],
         fc_sds[, 1])
  }

c(param_distributions,
  param_samples,
  fc_means,
  fc_sds) %<-% fit_vi(
    ts,
    ts_train,
    mannequin,
    n_iterations,
    n_param_samples,
    n_forecast_steps,
    n_forecast_samples
  )

For brevity, we haven’t computed smoothed and/or filtered estimates for the general mannequin. On this instance, this being a sum mannequin, we need to present one thing else as an alternative: the way in which it decomposes into parts.

However first, the forecasts:

forecast_df <- df %>%
  add_column(pred_mean = c(rep(NA, size(ts_train)), fc_means)) %>%
  add_column(pred_sd = c(rep(NA, size(ts_train)), fc_sds))


ggplot(forecast_df, aes(x = month, y = n)) +
  geom_line(coloration = "gray") +
  geom_line(aes(y = pred_mean), coloration = "cyan") +
  geom_ribbon(
    aes(ymin = pred_mean - 2 * pred_sd, ymax = pred_mean + 2 * pred_sd),
    alpha = 0.2,
    fill = "cyan"
  ) +
  theme(axis.title = element_blank())

Determine 6: AirPassengers, 12-months-ahead forecast.

A name to sts_decompose_by_component yields the (centered) parts, a linear development and a seasonal issue:

component_dists <-
  ts_train %>% sts_decompose_by_component(mannequin = mannequin, parameter_samples = param_samples)

seasonal_effect_means <- component_dists[[1]] %>% tfd_mean()
seasonal_effect_sds <- component_dists[[1]] %>% tfd_stddev()
linear_effect_means <- component_dists[[2]] %>% tfd_mean()
linear_effect_sds <- component_dists[[2]] %>% tfd_stddev()

with(tf$Session() %as% sess, {
  c(
    seasonal_effect_means,
    seasonal_effect_sds,
    linear_effect_means,
    linear_effect_sds
  ) %<-% sess$run(
    checklist(
      seasonal_effect_means,
      seasonal_effect_sds,
      linear_effect_means,
      linear_effect_sds
    )
  )
})

components_df <- forecast_df %>%
  add_column(seasonal_effect_means = c(seasonal_effect_means, rep(NA, n_forecast_steps))) %>%
  add_column(seasonal_effect_sds = c(seasonal_effect_sds, rep(NA, n_forecast_steps))) %>%
  add_column(linear_effect_means = c(linear_effect_means, rep(NA, n_forecast_steps))) %>%
  add_column(linear_effect_sds = c(linear_effect_sds, rep(NA, n_forecast_steps)))

ggplot(components_df, aes(x = month, y = n)) +
  geom_line(aes(y = seasonal_effect_means), coloration = "orange") +
  geom_ribbon(
    aes(
      ymin = seasonal_effect_means - 2 * seasonal_effect_sds,
      ymax = seasonal_effect_means + 2 * seasonal_effect_sds
    ),
    alpha = 0.2,
    fill = "orange"
  ) +
  theme(axis.title = element_blank()) +
  geom_line(aes(y = linear_effect_means), coloration = "inexperienced") +
  geom_ribbon(
    aes(
      ymin = linear_effect_means - 2 * linear_effect_sds,
      ymax = linear_effect_means + 2 * linear_effect_sds
    ),
    alpha = 0.2,
    fill = "inexperienced"
  ) +
  theme(axis.title = element_blank())

AirPassengers, decomposition into a linear trend and a seasonal component (both centered).

Determine 7: AirPassengers, decomposition right into a linear development and a seasonal part (each centered).

Wrapping up

We’ve seen how with DLMs, there’s a bunch of fascinating stuff you are able to do – aside from acquiring forecasts, which most likely would be the final purpose in most functions – : You possibly can examine the smoothed and the filtered estimates from the Kálmán filter, and you’ll decompose a mannequin into its posterior parts. A very engaging mannequin is dynamic linear regression, featured in our first instance, which permits us to acquire regression coefficients that change over time.

This publish confirmed easy methods to accomplish this with tfprobability. As of at this time, TensorFlow (and thus, TensorFlow Chance) is in a state of considerable inside adjustments, with wanting to develop into the default execution mode very quickly. Concurrently, the superior TensorFlow Chance growth staff are including new and thrilling options every single day. Consequently, this publish is snapshot capturing easy methods to finest accomplish these targets now: When you’re studying this a number of months from now, chances are high that what’s work in progress now can have develop into a mature methodology by then, and there could also be quicker methods to achieve the identical targets. On the fee TFP is evolving, we’re excited for the issues to return!

Berndt, R. 1991. The Observe of Econometrics. Addison-Wesley.

Murphy, Kevin. 2012. Machine Studying: A Probabilistic Perspective. MIT Press.

Petris, Giovanni, sonia Petrone, and Patrizia Campagnoli. 2009. Dynamic Linear Fashions with r. Springer.

Supply hyperlink

Dynamic linear fashions with tfprobability

Dynamic linear regression instance: Capital Asset Pricing Mannequin (CAPM)

Becoming a time sequence with variational inference

Acquiring forecasts

Smoothing and filtering (Kálmán filter)

Placing all of it collectively (the VI version)

Placing all of it collectively (the HMC version)

Forecasts

Smoothing estimates

Filtering estimates

Composition instance: AirPassengers

Wrapping up

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